a comparative analysis of contact metrology devices … · a comparative analysis of contact...

A Comparative Analysis Of Contact Metrology Devices Versus Non-Contact Metrology

Systems Utilizing Structured Light

by

Michael Hestness

A Research Paper Submitted in Partial Fulfillment of the

Requirements for the Master of Science Degree

in

Manufacturing Engineering

The Graduate School

University of Wisconsin-Stout

December 2011

Copyright 2011 Lockheed Martin

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The Graduate School University of Wisconsin-Stout

Menomonie, WI 54751

Author: Hestness, Michael L.

Title: A Comparative Analysis of Contact Metrology Devices versus Non-

Contact Metrology Systems Utilizing Structured Light

Graduate Degree/ Major: Master of Science in Manufacturing Engineering

Research Adviser: John Dzissah

Month/Year: December, 2011

Number of Pages: 86

Style Manual Used: American Psychological Association, 6th

edition

Abstract

Modern aerospace manufacturing processes utilize the most advanced techniques

and technologies in the world. The need for lower cost and more efficient aircraft is

pushing the limits of current material and process capabilities. In an effort to meet these

requirements, aircraft designs require tighter tolerances than ever to ensure quality,

safety, and performance. While this may result in efficient airplanes with lower

operating costs, the initial costs to manufacture parts meeting stringent tolerances and

the subsequent inspection methods drive up production costs.

To meet these challenges, improved manufacturing and inspection techniques

are required. Traditionally, coordinate measuring machines (CMMs) and laser trackers

have been the metrology workhorses of aerospace. While these technologies are

accurate, they are slow and can be labor intensive to program and operate. Recent

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advances in non-contact inspection technologies such as white light, or structured light,

have produced impressive results but there is limited data to demonstrate these

technologies are as accurate as the legacy methods. This paper will compare the

capability of three white light systems to the capability of contact metrology

measurement devices by conducting a gage linearity and bias study and a gage

repeatability and reproducibility study for each white light scanning system.

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The Graduate School

University of Wisconsin Stout Menomonie, WI

Acknowledgments

I would like to thank my wife Amy and our kids for their support and patience

during my time in school. I would also like to thank the faculty of UW-Stout, especially

David Fly, for the support he and everyone has provided to me as a distance learning

student. Last but not least, I would like to thank Dr. John Dzissah for his time and

guidance completing this paper.

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Table of Contents

.................................................................................................................................. Page

Abstract ........................................................................................................................... 2

List of Tables ................................................................................................................... 7

List of Figures .................................................................................................................. 8

Chapter I: Introduction ..................................................................................................... 9

Statement of the Problem ................................................................................... 11

Purpose of the Study .......................................................................................... 12

Assumptions of the Study ................................................................................... 13

Limitations of the Study ...................................................................................... 13

Definition of Terms ............................................................................................. 14

Methodology ....................................................................................................... 15

Chapter II: Literature Review ......................................................................................... 17

Early Metrology Tools ......................................................................................... 17

Coordinate Measuring Machines ........................................................................ 19

Non-Contact Metrology ....................................................................................... 22

Gage Repeatability and Reproducibility .............................................................. 26

The Future of Non-contact Inspection ................................................................ 27

Chapter III: Methodology ............................................................................................... 28

Equipment .......................................................................................................... 28

Features and Test Articles .................................................................................. 32

Benchmarking ..................................................................................................... 35

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Non-contact Data Collection ............................................................................... 36

Gage Repeatability and Reproducibility Study ................................................... 44

Chapter IV: Results ....................................................................................................... 45

Results ............................................................................................................... 45

Summary of Findings .......................................................................................... 45 Gage Linearity and Bias Study ........................................................................... 46 Gage Repeatability and Reproducibility Study ................................................... 47

Chapter V: Discussion ................................................................................................... 50

Conclusions ........................................................................................................ 50

Recommendations .............................................................................................. 50

References .................................................................................................................... 52

Appendix A: Spot Face Panel Drawing ........................................................................ 55

Appendix B: Spot Face Panel Benchmark Results ....................................................... 56

Appendix C: Countersink Test Coupon Dimensions ..................................................... 57

Appendix D: Precision Surface Block ............................................................................ 58

Appendix E: Two-Ball, Ball Over Method Formula ........................................................ 59

Appendix F: Input Data and Results for Precision Surface Panel ................................. 60

Appendix G: Input Data and Results for Spot Face Panel ............................................. 69

Appendix H: Input Data and Results for Countersink Panel .......................................... 78

.

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List of Tables Table 1: Test Article Tolerances .................................................................................... 46 Table 2: Average Bias of Each Measurement System .................................................. 46 Table 3: Linearity Values for Each System Measuring and Each Test Article ............... 47 Table 4: %Linearity Values for Each System Measuring and Each Test Article ............ 47 Table 5: Gage R&R Repeatability Results .................................................................... 48 Table 6: Gage R&R Reproducibility Results .................................................................. 48 Table 7: Total Gage R&R Variation as A Percent of the Tolerance .............................. 48 Table 8: Acceptability of the Measurement Systems Based On 30% Variation/Tolerance

Ratio ................................................................................................................ 49

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List of Figures

Figure 1: Micrometer ..................................................................................................... 18 Figure 2: Vernier caliper ................................................................................................ 19

Figure 3: Zeiss Accura II AKTIV CMM ........................................................................... 29 Figure 4: CogniTens WLS 400M ................................................................................... 30 Figure 5: ATOS Triple Scan .......................................................................................... 31 Figure 6: Rexcan 4 White Light Scanner ....................................................................... 32 Figure 7: Precision Surface Block ................................................................................. 33 Figure 8: Spot Face Panel ............................................................................................. 34 Figure 9: Countersink Panel .......................................................................................... 35 Figure 10: Calibrating the WLS 400M ........................................................................... 37 Figure 11: Measuring the Precision Surface Block with the WLS 400M ........................ 38 Figure 12: Measuring the Spot Face Panel with the WLS 400M ................................... 38 Figure 13: Measuring the Countersink Panel with the WLS 400M ................................ 39 Figure 14: Calibrating the Rexcan 4 .............................................................................. 40 Figure 15: Mapping the Artifact with Photogrammetry................................................... 40 Figure 16: Measuring the Precision Surface Block with the Rexcan 4 ......................... 41 Figure 17: Measuring the Spot Face Panel with the Rexcan 4 ...................................... 41 Figure 18: Measuring the Countersink Panel with the Rexcan 4 ................................... 42 Figure 19: Measuring the Precision Surface Block with the ATOS Triple Scan ............. 42 Figure 20: Measuring the Spot Face Panel with the ATOS Triple Scan ........................ 43 Figure 21: Measuring the Countersink Panel with the ATOS Triple Scan ..................... 43

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Chapter I: Introduction

Many of today’s products have been designed with manufacturing tolerances that

were unachievable just ten years ago. As modern manufacturing processes improve

and become capable of producing these extremely precise parts, it becomes imperative

to have methods of inspection that are precise and capable of rapidly inspecting these

parts.

The automotive and aerospace industries have typically been the main users of

very precise and highly inspected parts. Components in an engine, whether it is an

automotive engine or a jet engine, are often held to tolerances in the ten thousandths of

an inch range. To measure these parts accurately, coordinate measuring machines

(CMMs) have long been considered a gold standard in metrology, which is the study of

measurements. A CMM is a measurement device used to make three-dimensional

measurements (Kalpakjian, 1992) and can come in several forms, but a typical bridge

CMM consists of a granite bed, a touch probe, and a gantry system allowing very

precise control of the touch probe in three axes.

As the aerospace industry develops larger aircraft with increasingly tight

tolerances to achieve the desired performance, the need to build larger CMMs becomes

clear. These new CMMs are very expensive because as the inspection volume

increases, so does the cost. Many CMMs used in aerospace are housed in

environmentally controlled buildings. Larger CMMs require larger buildings and more

controls to maintain a constant room temperature. Also, as the machines become

larger they need to be made more rigid and sit on sturdier foundations.

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A long Y-axis bridge is more susceptible to bending and vibrations than a short

bridge. In addition, the further the CMM travels from its home or start position (x=0,

y=0, z=0), the more likely it is to lose accuracy. To compensate for this, large CMMs

need better encoders and specialized software to ensure they remain accurate over a

large volume.

Lockheed Martin is producing the F-35 Joint Strike Fighter and is expected to

produce one plane per day when full rate production is realized. As the only 5th

generation fighter jet in production, no other airplane in the world is manufactured to the

stringent tolerances required to ensure the plane meets its performance goals. A 5th

generation plane is considered a low observable (LO) plane and is more commonly

known as a stealth plane. LO planes are designed to be nearly invisible to radar. To

achieve LO performance, the plane utilizes unique shape characteristics, advanced

materials, and special coatings. All of these must be strictly controlled and verified.

The use of the digital thread (Kinard, 2010) has also added to the challenges of

aerospace manufacturing. Coined by Lockheed Martin, the term digital thread means

there are no master tools used to build parts. Every part produced is controlled by a

three-dimensional computer model. Legacy aircraft started with engineering drawings,

but those drawings were turned into gage tools that superseded the drawings. Once

parts were produced they could always be compared back to the gage tools if there was

a problem with the way that parts fit to the aircraft. Legacy aircraft also used drill

fixtures and routing jigs to shape parts and drill holes. With the digital thread, the hard

tooling is gone.

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While the benefits of the digital thread outweigh the challenges associated with it,

when there is a problem with components fitting together during the assembly process it

now becomes very difficult to tell which part is out of tolerance. This leads to the need

to inspect large assemblies such as landing gear doors, hinge assemblies, and aircraft

door openings. Measuring components like this is not something CMMs were ever

intended to do and even if an assembly such as a landing gear door was fixtured to a

CMM bed, it would take far too long to inspect enough points to get to the root cause of

the part mismatch. This has led to the development of new non-contact inspection

methods such as coherent laser radars, structured white light, and laser scanners.

While these new systems are developing rapidly, there is limited data supporting that

the accuracy of these systems is comparable to traditional CMMs. The advantage to

these non-contact systems is they are portable, much less expensive, record millions of

data points in a matter of seconds, and do not require labor intensive numerical control

programming.

While the coordinate measuring machine will retain its need in industry for a long

time to come, there is an urgent need to advance and validate the capabilities of non-

contact inspection methods in the manufacturing world.

Statement of the Problem

Coordinate measuring machines utilizing touch-probes are capable of recording

very accurate and precise measurements, but they are expensive, take up valuable

floor space, are sensitive to vibration, and only collect one point at a time; they are not

considered to be a rapid inspection technology. Non-contact inspection technologies

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exist that may provide solutions to these challenges, but there is limited data to support

that the non-contact systems are as accurate as touch-probe CMMs.

Purpose of the Study

The purpose of this study is to quantify the accuracies of non-contact metrology

systems utilizing structured light. Coordinate measuring machines are considered to be

one of the most capable metrology instruments available (Chapman, 2002), and are

traceable to the National Institute of Standards and Technology (NIST) standards

(Morey, 2010a), however, they are not without limitations. They require dedicated floor

space, computer programs to run them, special fixturing for the parts to be inspected,

are sensitive to vibrations, and only record a single data point each time they touch a

part. Although touch probes are capable of recording up to 60 points per minute

(Chapman, 2002), this is not fast enough for certain manufacturing applications.

Non-contact technology utilizing structured light technology, commonly referred

to as white light technology, uses a light source to project a two-dimensional shadow

pattern on a three-dimensional surface. Digital cameras then record the distorted image

that result from the contours and features of the three-dimensional surface. Powerful

software takes this image and calculates the distance the cameras are from the surface

and thus measures the part. Some of the newer systems now use blue light-emitting-

diodes (LEDs) to produce the two dimensional pattern. Advantages of blue light will be

discussed later.

According to Marc Demarest (Morey, 2010a), structured light technology has

been around since 1995, so this isn’t new technology. However, it has been gaining

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notoriety and feels like new technology to many users. As with any technology that is

new to a user, it is important to establish a baseline for the current state of the art and

then qualify the new technology to existing standards ensuring equivalency of

performance.

The deliverables of this study consist of gage linearity and bias study validating

the accuracy of the structured light measurement systems, and a gage repeatability and

reproducibility (gage R&R) study validating the robustness of the measurement process.

Assumptions of the Study

Gage R&R studies were not conducted on the contact metrology devices. The

CMM was calibrated in compliance with DIN EN ISO 10360-2:2001 on January 26,

2011and was accurate within 3 microns/meter. The Fowler height gage was within the

calibration certification date and verified with a NIST traceable standard. The two-ball-

over method used to measure the countersinks is considered an acceptable method of

calibrating countersink gages at Lockheed Martin.

Minitab gage R&R results no longer utilize 5.15 standard deviations to calculate

gage variation. Minitab 16 calculates study variation as 6 times the standard deviation

of each variation source in accordance with recommendations from the Automotive

Industry Action Group (AIAG) (Minitab Inc, 2010).

Limitations of the Study

This study was limited by several factors. The amount of time the CMM was

available to measure and baseline the test artifacts meant that test artifacts had to be

selected with measurement capacity in mind. Two artifacts that had originally been

scoped had to be eliminated from testing because the large CMM required to measure

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them was not available. CMM programmer time also affected the selection of artifacts.

Due to the lack of CMM capacity, a gage R&R was not performed for any of the contact

measurement processes. The artifact measured on the CMM was only measured once,

as was the case for the spot face panel and the countersink panel. Budget constraints

had an effect on the number of white light scanners tested and a reduction in manpower

lead to down-scoping the original project.

The time it took to analyze the data was also a limiting factor in this study. While

collecting data can be accomplished relatively quickly, analyzing point cloud data can

take a considerable amount of time. Once the process of isolating the desired features

from rest of the point cloud has been defined, an algorithm can be developed to

automate the process. However, defining the initial process can be time consuming and

may require multiple algorithms for features such as varying hole diameters.

Definition of Terms

Benchmark. “A standard by which something can be measured or judged”

(“Benchmark,” n.d., para. 1).

Bias. The difference between the measured point and the reference point

(Minitab, 2010)

Bridge-type CMM. “A type of CMM with a horizontal beam holding the probe.

The bridge-type CMM is the most common type” (“Basics,” 2011, para. 3).

Calibrated. When an instrument is compared to a more accurate instrument to

indentify and correct measurement errors (Goldsmith, 2010).

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Coordinate measuring machine. “A sophisticated measuring instrument with a

flat polished table and a suspended probe that measures parts in three-dimensional

space” (“Basics,” 2011, para. 3).

Digital thread. “Digital thread implies that 3D exact solid models from

engineering design are used directly by manufacturing for NC programming, coordinate

measurement machines inspections, and tooling (which are also 3D solid models)”

(Kinard, 2010, para 3).

Gage repeatability and reproducibility. Also know as gage R&R, it is a study

to “determine the magnitude of the variation in a measurement system as well as the

sources of this variation” (Kappele and Raffaldi, 2005, para 2).

Linearity. Linearity is an expression of the accuracy of the gage throughout the

expected range of measurements (Minitab, 2010)

Metrology. “The science that deals with measurement” (“Metrology,” n.d., para.

1).

Micron. One millionth of one meter. (“Micron,” n.d., para. 1).

Structured light. A pattern of light projected from either a white light source or a

blue light source onto a three dimensional surface (Morey, 2010).

Traceable. The ability to connect a measurement to a national or international

standard through an unbroken set of comparisons (Goldsmith, 2010).

Methodology

The researcher conducted a gage linearity and bias study for three structured

white light systems and compared them to contact metrology methods including a Zeiss

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Accura II AKTIV CMM, a Fowler digital indicator (Model 54-520-777) attached to a

height gage, and a two-ball-over method of measurement. Three test articles were

benchmarked with the contact measurement methods and then measured with a

CogniTens WLS400M Blue Light Scanner, a GOM ATOS Triple Scan with Blue Light

Technology, and a Rexcan 4 white light scanner by Solutionix. In addition to performing

the gage linearity and bias study to validate accuracy, a gage repeatability and

reproducibility (gage R&R) study was conducted. The gage R&R utilized three different

operators to measure the three test articles, each containing twenty-one measurement

points/features. Each operator measured each article three times. The results of the

gage R&R were evaluated with Minitab 16.0.0 software using the ANOVA method.

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Chapter II: Literature Review

As manufacturing technologies mature, and design engineers create larger and

more complex structures, the need for improved inspection technologies becomes

increasingly important. Traditional inspection equipment and processes are capable of

performing very accurate and very precise measurements, but these processes are

often relatively slow and very expensive. In addition, these processes only inspect

discrete points that may not fully reflect the condition of the part. When analyzing why

parts do not fit together properly in an assembly it is helpful to have surface data rather

than single point inspection data. A cloud of surface points may reveal a wavy, uneven

surface while the individual points gathered from a traditional CMM may be coincidently

spaced such that they reveal a near perfectly flat surface.

Three-dimensional non-contact inspection technologies have been in use for

over fifteen years but they are just now becoming capable of meeting the high accuracy

and high precision requirements of modern manufacturing industries such as

automotive and aerospace (Morey, 2010a). As a result of improving capability there is a

rush to refine and utilize these technologies to reduce manufacturing costs as well

increase manufacturing throughput. This chapter will review the current body of

literature and discuss early metrology tools, coordinate measuring machines, non-

contact metrology systems used in industry, gage repeatability and reproducibility, and

finally comments on the future of non-contact metrology.

Early Metrology Tools

The need for standard measurement devices has existed for nearly all of history

(Bucher, 2004). Methods of measuring distance were often a function of a body part

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such as the length of an arm or the width of a hand. Time was measured by cycles of

the sun, the moon, and the appearance of specific stars. Standard methods of

measuring food and liquids were required for cooking and bartering.

According to Bucher (2004), the use of calibrated and traceable measurement

standards goes as far back as the Egyptians. The royal cubit was made from granite

and was used as the official measurement standard. From this standard, additional

cubits were made of wood and then used in construction throughout the land. To

ensure the accuracy of the wooden cubits used by the workers, at each full moon the

cubits were returned to the royal architect and compared to the royal cubit. The

punishment for failing to return a wooden cubit was death. Because the Egyptians had

this strict system of standards they were able to build the Great Pyramid of Giza to an

accuracy of 4 ½ inches over 756 feet.

Brown and Sharpe introduced the first micrometer to the public in 1867 (Roe,

1916). While they had been in use by machinists before this time, this marked the first

time it was produced in mass. A micrometer is a handheld device consisting of a frame,

anvil, sleeve, spindle, thimble and a ratchet (Figure 1). Modern micrometers are

considered to be very precise and are accurate to ± .00005 in. (Starrett, 2011a).

Figure 1. Micrometer. Retrieved from http://www.technologystudent.com/equip1/microm1.htm

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While not as precise or accurate as a micrometer, another extremely useful hand

tool is the vernier caliper (Figure 2). Calipers can measure external features, internal

features, and depths to an accuracy of ±.0001 in. (Starrett, 2011b).

Figure 2. Vernier caliper. Retrieved from http://www.technologystudent.com/equip1/vernier3.htm

There are many useful hand tools available to machinists to make extremely

accurate and precise measurements, but for many of the manufacturing processes

employed today, hand tools are not adequate (Logee, Fabiano, & Cassola, 2009).

Whether it is the potential of human error, the number of points being inspected, or

shape of the part, many times automation in metrology is required.

Coordinate Measuring Machines

The coordinate measuring machine (CMM) has long been considered the

standard in metrology (O’Rourke, 2011). CMMs have been in existence since the

1960’s but have been continually improved and enhanced. According to Mark Bliek of

Bolton Works, “CMMs are still the workhorses for geometric quality control. The

calibration of a CMM can be traced back to NIST standards, and therefore the industry

is comfortable with the measurements made with a CMM” (Morey, 2010a, p.57).

Bucher (2004) says that using traceable standards for calibration is essential to

ensuring consistent parts. Using a traceable standard means that any measurement

standard used in the calibration of a measurement device can be verified by a more

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accurate measurement standard which can be compared to a master standard held by

an organization such as NIST. There has to be an unbroken chain of verification from

the master NIST standard down to the final equipment being calibrated. This does not

mean a machinist has to send their micrometer to NIST for calibration, but the

measurement standard they use must have been inspected by a more accurate

measurement standard that is traceable to a NIST standard. The use of a single master

measurement standard ensures that a .5 in. bolt in the United States is the same as a .5

in. bolt in Germany. This is ensured by the Bureau International des Poids et Mesures

(BIPM) in Sevres, France providing all countries with the same defined standards.

Originally CMMs were two-axis, manually driven machines. However, modern

CMMs are now computer controlled and have up to five axes or degrees of freedom

(Morey, 2010b). To ensure the CMM is performing as expected, there are several

standards that may be used to calibrate the CMM. ISO 10360-2, ASME B89.4.10360.2-

2008, and VDI/VDE 2617 are the most common standards used today (Card, 2003).

These standards are similar to each other, but they each specify a different number of

tests and different procedures for performing these tests. Over the years these

documents have adopted similar procedures to truly become standardized in areas

such as environmental controls. ISO 10360-2 used to require testing at specific

temperatures, but now just as in ASME B89.4.10360.2-2008 the user is free to choose

the environmental conditions. Nevertheless, in spite of the variations in these

documents, all standards require traceability to NIST artifacts or standards making the

CMM a highly regarded tool in metrology.

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CMMs come in a wide range of sizes, shapes and configurations (O’Rourke,

2011). There are small bench top CMMs that are still hand driven as well as ones that

have linear drive motors and digital readouts to make it easier to record measurements.

There are portable CMMs such as arms. The Faro Edge ScanArm weighs 24 pounds

and can be mounted to a bench top or movable stand. Mraz (2011) says the Faro arm

comes in three models that have working envelope ranges of 6 ft, 9 ft, and 12 ft. The 6

ft model has a stated single-point repeatability of .0009 in. while the 12 ft model is

repeatable to .0025 in. This means an operator measuring the same point two times

with the 6 ft model can find the same point to an accuracy of .0009 in. In addition to the

bench top and portable models, companies like Lockheed Martin have CMMs with

machine beds that that are 16 meters long, 5 meters wide, and capable of measuring

parts 2.5 meters tall with a linear accuracy of 10 microns per meter (B. Kush, personal

communication, August 1, 2011).

Coordinate measuring machines are very accurate and useful for measuring

parts, but each part measured requires its own computer program to run the machine.

Some parts may only take a couple of hours to program but Mark Boucher (2007) of

NewCastle Measurement says developing CMM a program for an extremely complex

part could take several days to complete. In addition to the lengthy programming time

the CMM also may require numerous calibration routines to be developed to account for

and reduce the risk of probe offset errors. Programming can often be performed off line

which saves CMM time, but still requires the expense of a CMM programmer. Software

is constantly being improved to make programming easier, but often the CMM programs

are not compatible with CMMs from different manufacturers. This means if a shop has

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multiple CMMs from different manufacturers, the CMMs must have dedicated parts or

multiple programs will be required. CMMs are very useful, but not always flexible and

accommodating to change.

Non-Contact Metrology

As the automotive, aerospace, and wind turbine industries move forward with

more advanced designs and manufacturing processes, it becomes clear that even

though the CMM will not be disappearing anytime soon, new methods of inspection will

be required. The aerospace industry and the wind turbine industry both use very large

monolithic composite structures. According to Zach Rodgers at Nikon Metrology,

whether it is a barrel section of a commercial plane’s fuselage, or a long wind turbine

blade, measuring large structures like these is just not feasible with a CMM (Morey,

2011, p. 59-60). In both automotive and aerospace there is a need to rapidly inspect

complex shapes that would be impractical to fixture on a CMM (Morey, 2010b). To

measure parts in this complex environment, the need for non-contact inspection

technologies is rapidly growing.

In 2004, Lockheed Martin Aeronautics determined they had to develop an

alternative approach to controlling the thickness of composite wing skins (P. Briney,

personal communication, July 7, 2011). Lockheed’s previous approach utilized very

expensive milling machines to profile the inner mold line of the laminate which left

structurally unnecessary material on the part resulting in excessive weight. To

overcome this challenge, the cured laminate compensation (CLC) process was

developed. The CLC process was made possible through the use of the Nikon MV-224

Coherent Laser Radar (CLR).

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The MV-224 is a form of non-contact CMM and has been replaced by the next

generation Nikon MV-330. Originally developed by Coleman Research in the early

1990’s, the laser radar has been in continual use and undergoing improvements ever

since (P. Morken, personal communication, July 7, 2011). According to the Nikon Laser

Radar Training Workbook (2010) the MV330 operates on a principle similar to the

conventional radar principle of time-of-flight. To do this, a laser beam is split into two

beams with one beam traveling through a reference coil of known length and the other

beam traveling to the surface. Distance is calculated by measuring the time it takes for

the laser signal to reflect back from the surface being measured, and comparing this

signal to the time it takes the second signal to travel though the reference coil. Since

time-of-flight can be difficult to measure, the laser signal is modulated to vary its

frequency. The change in the frequency has a direct relationship to time-of-flight and is

much easier to measure. It is through the development of proprietary software that

Nikon has been able to achieve this. The MV-330 has a stated accuracy of .001 in. at a

distance of 6 ft, and .004 in. at 100 ft. It was this level of accuracy and the

programmable non-contact features that enabled Lockheed Martin to develop a solution

to their composite thickness challenge. The Laser Radar can be operated in a manual

mode or programmed just like the touch-probe CMMs, enabling them to consistently

measure the same discrete points. Jeff Drewett of Lockheed Martin says the CLR

makes the CLC process repeatable to .0013 in. on composite surfaces and .0008 in. on

an Invar cure tool surface (Morey, 2011, p. 63).

The latest technology to make headlines in metrology is structured light.

Structured light is not a new technology, but it has gained notoriety in the last couple of

24


years (Morey, 2011). It is commonly referred to as white light scanning even with the

recent introduction of blue light scanners. Hexagon Metrology has unveiled a new

scanner utilizing blue light-emitting-diodes (LEDs) as the light source, but they branded

the product as the WLS400 to take advantage of the white light name.

The terms white light system and structured light system have become a sort of

catch-all for several technologies (3D Surface Reconstruction Technologies, 2008).

Currently there are systems utilizing white light and systems using blue light. White light

was the first to be developed, but white light systems can be affected by shiny surfaces.

Blue light scanners filter out all wave-lengths except blue light, and are therefore less

sensitive to ambient light and highly reflective surfaces. The CogniTens WLS400M blue

light scanner has been shown to produce less measurement variation than the

CogniTens Optigo 200 which uses white light (C. Bliss, personal communication, July

26, 2011).

In addition to the difference in the light source, there is also a difference in the

way the data is collected. While structured light is often used to describe these

systems, some systems use structured light and others use stereo vision (3D Surface

Reconstruction Technologies, 2008). Structured light projects a series of moving gray

stripes on a three dimensional surface. As the pattern distorts over the three

dimensional surface, high resolution digital cameras record the data (Morey, 2011).

With the aid of powerful software, the shape of the object is determined from the

distorted gray stripes. Structured white light systems can utilize one, two, or three

imaging cameras and a light source. Structured light systems can produce very good

data with little variation, but the time it takes to acquire the data can be two or three

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seconds. Because of this, structured light systems must be very stable as they are

susceptible to vibrations. If the part moves or the camera system moves while the data

is being acquired, the recorded image will be inaccurate. Stereo vision systems use two

or three digital imagining cameras to record the data, but rather than project a moving

two-dimensional pattern of gray bars over the three-dimensional surface, stereo vision

systems project a static pattern (3D Surface Reconstruction Technologies, 2008). If the

two cameras are able to see a common point in two stereo images, they can use

triangulation to calculate the location of the point in space. Because the projected

image does not move and the camera acquires the image in milliseconds, this type of

white light system is much less sensitive to vibration. However, the trade off is the data

acquired has more variation than the structured light system. Because of this, the

application must be considered. While stereo vision may not provide as precise data as

structured light, it works very well in a factory floor environment.

The manufactures of structured light systems claim the accuracy is equivalent to

touch-probe CMMs, but with the exception of the work by Hammett and Garcia-Guzman

(2006), few significant studies have been carried out to date. Also, as Hammett and

Garcia-Guzman (2006) conclude, there is more to determining the accuracy of a system

than just measuring an artifact. The entire process of acquiring the data must be

considered. According to a study by Hammett, Guzman, Frescoln, and Ellison (2005),

one must consider how the data is collected. One advantage of non-contact systems

over traditional CMMs is the part can often be measured without tooling or fixtures to

hold the part. In these examples, the data is analyzed relative to the part model and

can be extremely accurate. However, if the part is held in a fixture and analyzed

26


relative to a fixtured coordinate system, the capability of the system is reduced.

Hammett theorizes that 80-90% of the part variation will come from the set-up in the

fixture. Because of this, great care must be taken to ensure the right process and

measurement systems are used for each specific application.

Gage Repeatability and Reproducibility

To validate a measurement process, a gage repeatability and reproducibility

(gage R&R) study should always be performed (Sloop, 2009). A gage R&R study is

designed to identify how much variation is in a measurement process and where the

variation comes from. Measurement variation comes from three main sources and it is

important to identify how much variation comes from each source. By knowing the

sources of variation, it may be possible to improve the measurement process (Kappele

& Raffaldi, 2005).

While there is not one single standard for performing a gage R&R, a typical study

consists of three operators, measuring ten features or parts, three times (Morey,

2010c). Repeatability is measured by the consistency of each operator’s

measurements. Reproducibility is measured by the degree to which the different

operator’s measurements agree with each other. High reproducibility with low

repeatability could indicate the operators need more training, while high repeatability

compared to low reproducibility could indicate poor equipment (Kappele & Raffaldi,

2005). Performing a gage R&R is a critical step to building confidence in the

measurement process.

27


The Future of Non-Contact Inspection

Non-contact metrology systems have made tremendous advances in the last ten

years and are taking on an increasing role in manufacturing (Morey, 2009). In-process

inspection is now being integrated as another part of the manufacturing process rather

than something performed only after the part is fabricated. By involving metrology

earlier in the manufacturing process, problems can be identified and corrected quickly to

minimize the cost of scrap and rework. It seems clear non-contact inspection systems

will continue to take on an expanding role in manufacturing. As more companies in the

manufacturing industry utilize this technology, it will be more critical than ever to have

standards and common processes for validating the capability of the equipment to

ensure it is matched with the proper application and need. There have been few

studies evaluating the performance of non-contact metrology relative to proven contact

metrology systems. However, with the increased interest and awareness of these

systems, it seems clear more and more data will be presented in the future.

28


Chapter III: Methodology

The purpose of this study was to evaluate the accuracy and repeatability of non-

contact metrology systems utilizing white light systems by comparing those results to

the accuracy and repeatability of coordinate measuring machines utilizing touch probes.

Coordinate measuring machines have a long history of reliable use and are traceable to

NIST standards, but they are expensive, relatively slow, and require dedicated floor

space. In some instances, they require environmentally controlled rooms. As various

industries produce larger monolithic structures such as wind turbine blades, airplane

fuselages, and wings, the need to control both cost and part quality with advanced,

rapid-inspection technologies becomes greater.

This chapter explains the process used to benchmark the capability of the

coordinate measuring machine, as well as how the data was collected with the non-

contact systems. In addition, it addresses the equipment used, how the test articles

were selected, and the number of operators collecting data. Finally, the method of the

data analysis for the gage R&R study will be discussed.

Equipment

There are many types of CMMs available and many types of white light systems

to choose from. For the baseline CMM test a Zeiss Accura II AKTIV CMM was used

(Figure 3). The Zeiss has an inspection bed that is 2 meters wide, 3 meters long and

can measure parts 1.5 meters tall. It has a stated linear accuracy of 10 microns per

meter, but calibration records indicate it is accurate to 1.5 microns per meter.

29


Figure 3: Zeiss Accura II AKTIV CMM

To evaluate non-contact white light systems, three different systems were

selected.

CogniTens WLS 400M White Light Scanner

ATOS Triple Scan with Blue Light Technology

Rexcan 4 White Light Scanner

The first system evaluated was a WLS400M white light system manufactured by

CogniTens, a subsidiary of Hexagon Metrology (Figure 3).

30


Figure 4. CogniTens WLS 400M

The WLS 400 uses blue light emitting diodes (LEDs) as a light source but is still

considered to be a white light system. Utilizing three cameras and stereo vision as the

method of data collection the WLS 400M projects a static pattern which is captured in

milliseconds and is therefore impervious to vibrations.

The second system used in the study was the ATOS Triple Scan with Blue Light

Technology by Gesellschaft für Optische Messtechnik (GOM) (Figure 4). The ATOS

Triple Scan was selected because it uses structured light and claims to have higher

accuracy and a lower signal-to-noise ratio, meaning the data should contain fewer

outliers than stereo vision systems. The ATOS uses blue LEDs as the light source and

has two cameras.

31


Figure 5. ATOS Triple Scan

The third white light system selected was the Rexcan 4 made by Solutionix. The

Rexcan 4 white light scanner is much less expensive than any of the other systems so it

was selected to evaluate what its capabilities are. This system uses two cameras and a

metal halide light source to project a white light pattern on the part and measure the

phase shift of the pattern.

32


Figure 6. Rexcan 4 White Light Scanner

Features and Test Articles

This study arose from the need to inspect specific features on large components

in a rapid manner with minimal to no fixturing. A list of these features was compiled and

used to drive the selection of the test articles.

The main features of concern were:

Surface profile

Fastener depth

33


Countersink diameter

To reduce the time and cost of designing and fabricating test articles, existing tools and

artifacts were used whenever possible.

Three test articles used were:

Precision Surface Block

Spot Face Panel

Countersink Panel

The first test article used was the precision surface block (Figure 6). This tool

had been fabricated several years earlier and provided a very stable tool to validate the

ability of the various systems to measure a surface profile. The 9 in. by 5 in. aluminum

block had three precision tooling balls bonded into bushings and a gentle contour

machined into the upper surface.

Figure 7. Precision Surface Block

The second tool selected was a simple gage tool and was referred to as the spot

face panel (Figure 7). It was fabricated from a Blanchard ground steel plate with a

34


series of spot faces machined into the surface to represent fastener heads. The

purpose of the gage tool was to provide a simple tool with as little variation as possible

to assess accuracy and repeatability. Two thirds of the tool was coated with black oxide

and one third of the tool was painted with green aircraft primer to evaluate the effects of

shininess. A drawing of the gage tool can be found in Appendix A.

Figure 8. Spot Face Panel

The last artifact, known as the countersink panel, was created from a 12 in.

square graphite-epoxy test panel. The test panel was .500 in. thick and had twenty-one

countersinks drilled into the surface and twelve non-countersunk holes. The

countersinks ranged from .350 in. to .750 in. in diameter. The panel was painted with

green aircraft primer, and the countersinks were machined after the primer was applied

so the resulting countersinks were black. This tool was originally fabricated to ascertain

the ability of white light systems to determine the depth and diameter of countersinks,

35


the diameter of drilled holes, and locations of both holes and countersinks, however,

due to time limitations only countersink diameters were evaluated. A drawing of the

panel is included in Appendix B.

Figure 9. Countersink Panel

Benchmarking

Each test article was built in accordance to a design model, but since the study

was not concerned with the capability of the machines producing the test articles, the

only values of concern were the as-built nominal values. Once the features were

measured by the contact measurement methods these were the values by which all

other analysis would be compared to. In order to evaluate the performance of the non-

contact systems it was critical to have reliable nominal values of the features and test

articles.

The first test artifact was the precision surface block. A computer model was

provided to the CMM programmer and an inspection routine was developed. Once the

CMM program was transferred to the CMM controller, the test article was inspected.

36


.

The surface and spot faces of the spot face panel were inspected with a Fowler

digital indicator (Model 54-520-777) attached to a height gage. The indicator was

zeroed on the top of the surface plate and then measurements were recorded in four

locations. These four measurement values were averaged to become the nominal spot

face depth.

To baseline the countersink panel a two-ball, ball over method was used to

measure the countersink diameter of the countersink at the surface. Using this method,

two spheres were placed one at a time in the countersink. With the sphere in the

countersink, the height of the sphere relative to the top of the countersink was recorded.

This process is repeated for the second sphere. Using the formula in Appendix F the

diameter of each countersink was calculated.

Non-Contact Data Collection

The collection of the non-contact measurement data took place over a course of

a two week period with one system tested the first week and two systems tested the

following week. The vendors shipped their equipment to the Lockheed facility on the

Friday before testing which meant Monday morning was spent unpacking and

calibrating the equipment. Measurements started Monday afternoon and continued

through Friday morning allowing for time to pack up the equipment and ready it for

shipment.

Measurement data from white light systems is captured by cameras and

triangulated by software, and therefore limited by the field of view of the camera at a

given focal length. To overcome this limitation, some systems utilized photogrammetry

37


to map the artifacts and provide a scaled reference frame. Each vendor utilized their

own photogrammetry equipment.

The first system tested was the CogniTens WLS 400M which was calibrated per

the manufacturer’s specifications prior to the start of the measurements.

Photogrammetry targets were placed on the parts and the table. The set-up was then

mapped to ensure multiple shots could be stitched together if required.

Figure 10. Calibrating the WLS 400M

The Precision Surface Block was placed on a sturdy steel table and then

measured by three operators. Each operator created three measurement collections

and each collection consisted of two shots from slightly different positions. By taking

two shots per collection the measurement point data was doubled to help ensure

sufficient data was collected. The WLS 400M is a portable system so prior to each

shot, the operator moved to camera to introduce some degree of variability into the

38


process. The actual acquisition of the measurement is taken by pointing the camera at

the part and pressing a button, so variation form the operators should be minimal.

Figure 11. Measuring the Precision Surface Block with the WLS 400M

The Spot Face Panel was the next artifact measured. This artifact was

measured in the same manner as the Precision Surface Block; three operators, each

taking three collections. As before, each collection consisted of two shots to ensure

adequate point density.

Figure 12. Measuring the Spot Face Panel with the WLS 400M

39


The last artifact to be measured was the countersink panel. Countersinks pose a

big challenge to non-contact systems. Without sufficient point data, the measurement

system may not actually collect a data point at the intersection of the edge of the

surface and the start of the countersink. Accurately measuring features such as

countersinks may require additional post processing software such as PolyWorks or

Geomagic. These software programs are better suited to best-fit a cone or similar

feature into the data for comparative purposes. While countersink measurement can be

a difficult task, it is one that aerospace manufacturers would benefit greatly from.

Figure 13. Measuring the Countersink Panel with the WLS 400M

During the second week of testing two systems were evaluated. The Rexcan 4

by Solutionix and the ATOS Triple Scan by GOM were both evaluated using the same

40


procedures as the previous two systems. For these two systems photogrammetric

mapping was used to stitch multiple images together. Once the parts were mapped, the

data acquisition followed with the Precision Surface Block first, the Spot Face Panel

second and the Countersink Panel last.

Figure 14. Calibrating the Rexcan 4 White Light Scanner

Figure 15. Mapping the Artifact with Photogrammetry

41


Figure 16. Measuring the Precision Surface Block with the Rexcan 4 White Light

Scanner

Figure 17. Measuring the Spot Face Panel with the Rexcan 4 White Light Scanner

42


Figure 18. Measuring the Countersink Panel with the Rexcan 4 White Light Scanner

Figure 19. Measuring the Precision Surface Block with the ATOS Triple Scan

43


Figure 20. Measuring the Spot Face Panel with the ATOS Triple Scan

Figure 21. Measuring the Countersink Panel with the ATOS Triple Scan

44


Gage Repeatability and Reproducibility Study

A gage repeatability and reproducibility (gage R&R) study should always be

performed for new inspection processes. The gage R&R is used to determine if the

measurement system is capable of providing correct inspection data for the tolerances

specified. As noted in the literature review, there is no single specification to guide the

gage R&R study. For this study three operators were selected to collect the data with

the non-contact systems. Each operator measured each test article three times,

capturing twenty-one features or measurement points. A matrix of the data collection

forms can be found in the Appendix of this report. Once the data was collected, Minitab

16.2.0.0 software was used to analyze the data using the ANOVA method. The output

from Minitab provided information on the range of the measurement variation, the

standard deviation of the results and a precision to tolerance ratio that indicates how

capable the measurement system is for the intended use. Linearity and bias data were

also provided.

45


Chapter IV: Results

Non-contact methods of inspection have been developed over the last fifteen

years, but there is limited data comparing the accuracy of these technologies to contact

methods of inspection. This study was conducted to make such a comparison.

Results

The analysis of this study was conducted using Minitab 16.2.0.0 software and is

contained in the appendix of this paper.

Summary of Findings

Three non-contact metrology systems were used to measure three distinct test

artifacts.

Non-contact Metrology Systems:

1. CogniTens WLS 400M White Light Scanner

2. ATOS Triple Scan With Blue Light Technology

3. Rexcan 4 White Light Scanner

Test Artifacts:

1. Precision Surface Plate

2. Spot Face Panel

3. Countersink Panel

The Precision Surface Plate evaluated the ability of each system to measure a

contoured surface and relate the points back to a nominal solid model. The Spot Face

Panel simulated measuring installed fastener depths, and the Countersink Panel tested

the ability to measure the diameter of a countersink. The Gage R&R was performed

46


utilizing the tolerances in Table 1 which are listed in inches. The full gage study results

are provided in the appendix of this paper.

Table 1: Test Article Tolerances Test Article Lower Tolerance Upper Tolerance

Precision Surface Block -.005” .005”

Spot Face Panel .000” .020”

Countersink Panel -.010” .010”

Gage Linearity and Bias Study

Gage linearity and bias are measurements of how accurate the measurement

system is relative to the baseline or reference value and how accurate it is across the

full range of intended measurements. Bias indicates the accuracy and linearity tells

how consistent the system is. The following table presents the bias of each system

relative to the nominal values. If the bias is higher than zero the system overestimates

the measurement and if the bias is negative it underestimates the measurement

(Minitab, 2007).

Table 2: Average Bias of Each Measurement System Test Article CogniTens WLS

400M ATOS Triple

Scan Rexcan 4 White

Light System Precision Surface Block -0.00010” 0.00015” 0.00028”

Spot Face Panel 0.00009” -0.00010” 0.00087”

Countersink Panel 0.00285” -0.00032” 0.00166”

Gage linearity describes how accurate the measurement system is across the

range of measurement process. Linearity is expressed as the slope of the

measurement bias as well as a %Linearity, which is linearity expressed as a percent of

47


the process variation (Minitab, 2007). Linearity data is provided in Table 3. Small

linearity values indicate the accuracy remains the same throughout the measurement

range.

Table 3: Linearity Values for Each System Measuring and Each Test Article Test Article CogniTens WLS

400M ATOS Triple

Scan Rexcan 4 White

Light System Precision Surface Block 0.00120 0.00175

0.00051

Spot Face Panel 0.00029 0.00056 0.00482

Countersink Panel 0.02110 0.01197

0.00140

%Linearity provides a metric to show measurement consistency. The closer %Linearity

is to zero, the more consistently the gage measures across the measurement range

(Minitab, 2007). Table 4 provides %Linearity data.

Table 4: %Linearity Values for Each System Measuring and Each Test Article Test Article CogniTens WLS

400M ATOS Triple

Scan Rexcan 4 White

Light System Precision Surface Block 2.20%

3.10%

0.90%

Spot Face Panel 0.07%

1.30%

12.10%

Countersink Panel 2.50%

1.50%

0.20%

Gage Repeatability and Reproducibility Study

A gage R&R study is useful to determine the amount of variation in a

measurement device or system as well as the source of the variation. Measurement

repeatability measures the variation between measurements taken by a single operator

and should be low. Table 5 provides the measurement repeatability for the systems.

48


Table 5: Gage R&R Repeatability Results Test Article CogniTens WLS

400M ATOS Triple

Scan Rexcan 4 White


0.01%

0.92%


0.21%

18.57%


0.01%

0.09%

Gage Reproducibility measures the amount of variation between operators

measuring the same part. Table 6 provides reproducibility data.

Table 6: Gage R&R Reproducibility Results Test Article CogniTens WLS

400M ATOS Triple

Scan Rexcan 4 White


0.00%

0.12%


0.10%

1.21%


0.03%

0.01%

Typically, the total gage variation for a measurement system would equal less

than 30% of the tolerance while less than 10% would be preferred (Minitab, 2007).

Table 7 provides the total gage variation as a percent of the tolerance.

Table 7: Total Gage R&R Variation as A Percent of the Tolerance

Test Article CogniTens WLS 400M

ATOS Triple Scan

Rexcan 4 White Light System

Precision Surface Block 15.18%

5.94%

55.40%


11.45%

88.88%


80.30%

179%

49


Table 8 provides a breakdown of measurement system acceptability based on a 30%

variation/tolerance ratio.

Table 8: Acceptability of the Measurement Systems Based On 30% Variation/Tolerance Ratio Test Article CogniTens WLS

400M ATOS Triple

Scan Rexcan 4 White

Light System Precision Surface Block Acceptable

Acceptable

Unacceptable

Spot Face Panel Acceptable

Acceptable

Unacceptable

Countersink Panel Unacceptable

Unacceptable

Unacceptable

50


Chapter V: Discussion

Non-contact metrology devices have made tremendous advances in the last ten

years and offer potential solutions to manufacturing challenges. Allowing quick

inspection of complex parts earlier in the manufacturing process offers the opportunity

to reduce scrap and increase part quality. Before any new inspection technology can

be utilized in an inspection process the system must be qualified for that given process.

This study evaluated the ability of three white light measurement systems to measure

surface contour, spot face depth (simulating fasters in a structure) and countersinks.

Conclusions

The results of this study show that the CogniTens WLS 400M and the GOM

ATOS Triple Scan are capable of accurately and repeatedly measuring surface profiles

and spot faces within the tested measurement tolerance range. However, neither

system was capable of measuring countersink diameters using point cloud data for

analysis. The Rexcan 4 White Light Scanner was not capable of achieving reliable

results for any of the measured test article.

Recommendations

The Rexcan 4 White Light Scanner was included in this study because it costs

significantly less than either the CogniTens or the ATOS scanners. Testing it against

the tight tolerances in this study showed it is not a capable instrument for this purpose.

However, given a wider tolerance band and different application, the Rexcan 4 may

show to be a cost effective alternative.

51


The CogniTens WLS 400M and GOM ATOS Triple Scan both proved to be

capable systems but each system has strengths and weakness that also need to be

considered for each application. While the ATOS system has slightly better results, the

ATOS system is sensitive to vibration and cannot be hand held. Since the CogniTens

WLS 400M acquires measurement data in milliseconds it can be hand held and can be

used in a wider range of production-type environments. In addition, the CogniTens

WLS 400M has a Vision Mode feature that allows the scanner to look for contrast

details on the surface. Countersink measurement data using this feature wasn’t

included in this study since the other two systems do not have it, but using the vision

mode offered much improved measurement data on the painted countersink panel.

However, if the panel had not been painted and there was not significant color contrast

between the countersink and the surface, it is not clear the vision mode advantage

would be maintained.

Collecting measurement data is relatively easy but knowing how to analyze takes

effort. While the ATOS and CogniTens systems were demonstrated to be capable,

there is certainly a need for improved algorithms to speed up the analysis of the data.

Now that the equipment has been shown to be capable, a follow-on study should be

conducted to analyze the measurement process to achieve optimized results.

52


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Morey, B. (2010c, November). Accuracy and uncertainty in non-contact metrology.

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55


Appendix A: Spot Face Panel Drawing

.: ---------!

56


Appendix B: Spot Face Panel Benchmark Results

Dia. (Column) A - Depth B - Depth C - Depth D - Depth Averaged Nominal 0.375” (1) 0.0006” 0.0006” 0.0007” 0.0006” 0.0006 0.375” (2) 0.0011” 0.0012” 0.0011” 0.0011” 0.0011 0.375” (3) 0.0038” 0.0039” 0.0037” 0.0034” 0.0037 0.375” (4) 0.0075” 0.0079” 0.0080” 0.0073” 0.0077 0.375” (5) 0.0113” 0.0118” 0.0118” 0.0112” 0.0115 0.375” (6) 0.0152” 0.0154” 0.0156” 0.0156” 0.0155 0.375” (7) 0.0192” 0.0193” 0.0197” 0.0195” 0.0194

0.500” (1) 0.0009” 0.0009” 0.0010” 0.0007” 0.0009 0.500” (2) 0.0014” 0.0016” 0.0018” 0.0015” 0.0016 0.500” (3) 0.0032” 0.0038” 0.0039” 0.0032” 0.0035 0.500” (4) 0.0073” 0.0080” 0.0080” 0.0070” 0.0076 0.500” (5) 0.0105” 0.0107” 0.0109” 0.0108” 0.0107 0.500” (6) 0.0156” 0.0154” 0.0156” 0.0159” 0.0156 0.500” (7) 0.0193” 0.0193” 0.0196” 0.0194” 0.0194

0.625” (1) 0.0013” 0.0016” 0.0015” 0.0012” 0.0014 0.625” (2) 0.0018” 0.0019” 0.0022” 0.0018” 0.0019 0.625” (3) 0.0031” 0.0043” 0.0032” 0.0031” 0.0034 0.625” (4) 0.0077” 0.0078” 0.0073” 0.0072” 0.0075 0.625” (5) 0.0122” 0.0120” 0.0115” 0.0115” 0.0118 0.625” (6) 0.0161” 0.0152” 0.0154” 0.0164” 0.0158 0.625” (7) 0.0195” 0.0184” 0.0187” 0.0194” 0.0190

57


Appendix C: Countersink Test Coupon Dimensions

58


Appendix D: Precision Surface Block

59


Appendix E: Two-Ball, Ball Over Method Formula

0 1 . d2

p,w: (H)-01/2 H I\2 ·d2/2l

1 0112 e: : 2 )( \ sin<J.: I) tanw • IH)-0112 X

60


Appendix F: Input Data and Results for Precision Surface Panel

CogniTens WLS 400M Input Data

Operator 1 Operator 2 Operator 3

Nominal Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3

0.1024 0.10419 0.10412 0.10415 0.10395 0.10389 0.10386 0.1041 0.10385 0.10371

0.0952 0.09654 0.09649 0.09658 0.09666 0.09617 0.09645 0.09658 0.09637 0.09621

0.0933 0.09437 0.09436 0.0944 0.09433 0.09403 0.09414 0.09458 0.09429 0.0942

0.0930 0.0938 0.09385 0.09395 0.09369 0.09366 0.09352 0.0939 0.09383 0.09421

0.0928 0.09342 0.09313 0.09344 0.09339 0.09305 0.09317 0.09335 0.09311 0.09421

0.0929 0.09285 0.09279 0.09299 0.09295 0.09246 0.09267 0.09296 0.09276 0.09371

0.0950 0.09444 0.09432 0.09453 0.09451 0.09407 0.09414 0.09466 0.09435 0.09491

0.0946 0.09544 0.09543 0.0955 0.09551 0.09532 0.09535 0.09555 0.09531 0.09512

0.0997 0.09995 0.10006 0.10005 0.10002 0.09977 0.0999 0.10012 0.09985 0.09974

0.1027 0.1027 0.10281 0.10282 0.10268 0.10249 0.10255 0.10311 0.10277 0.10297

0.1020 0.10195 0.10182 0.10202 0.10194 0.10168 0.10156 0.10218 0.10188 0.10283

0.0976 0.09691 0.09671 0.09698 0.0971 0.09658 0.09676 0.09706 0.09676 0.09775

0.0897 0.08896 0.0889 0.08908 0.08916 0.08865 0.0888 0.08927 0.08901 0.08927

0.0821 0.08098 0.08091 0.08117 0.0812 0.08068 0.08073 0.08132 0.08094 0.08106

0.1012 0.10141 0.10145 0.10149 0.10149 0.10133 0.10129 0.10158 0.10127 0.10113

0.1093 0.10884 0.1089 0.10891 0.109 0.10873 0.10891 0.10921 0.10885 0.1088

0.1120 0.11111 0.11121 0.11127 0.11126 0.11104 0.11104 0.11168 0.11127 0.11176

0.1080 0.10679 0.10691 0.10701 0.10708 0.10684 0.10665 0.10726 0.10692 0.10801

0.0977 0.09649 0.09653 0.09674 0.09687 0.09635 0.09653 0.0969 0.0966 0.097

0.0848 0.08329 0.08339 0.08352 0.08358 0.08324 0.08317 0.08384 0.08351 0.08347

0.0735 0.07225 0.07209 0.07243 0.07245 0.07195 0.0719 0.0727 0.07227 0.07232

61


0.110.100.090.080.07

0.002

0.001

0.000

-0.001

-0.002

Reference Value

Bia

s

0

Regression

95% CI

Data

Avg Bias

BiasLinearity

2

1

0

Pe

rce

nt

C onstant -0.0022090 0.0006800 0.001

Slope 0.021889 0.007041 0.002

Predictor C oef SE C oef P

Gage Linearity

S 0.0008577 R-Sq 4.9%

Linearity 0.0012239 %Linearity 2.2

A v erage -0.0001040 0.2 0.000

0.0735 -0.0012378 2.2 0.000

0.0821 -0.0011011 2.0 0.000

0.0848 -0.0013544 2.4 0.000

0.0897 -0.0006889 1.2 0.000

0.0928 0.0005633 1.0 0.003

0.0929 0.0000044 0.0 0.975

0.093 0.0008233 1.5 0.000

Reference Bias %Bias P

Gage Bias

Gage name: C ogniTens WLS 400M

Date of study : Nov 2011

Reported by : M. Hestness

Tolerance: ±.005

Misc: Precision Surface Block

Percent of Process Variation

Gage Linearity and Bias Study for Measured

Part-to-PartReprodRepeatGage R&R

400

200

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Point

6

Point

5

Point

4

Poin t

3

Point 21

Point 20

Point

2

Point 19

Point 1 8

Point 17

Point 16

Point 15

P oin t

14

Point 13

Point 12

Point 11

Point 10

Point

1

Point

9

Point

8

P oint

7

Point

6

Point

5

Point

4

Point

3

P oin t

21

Point 20

Point

2

Point 19

Point 18

Point 1 7

Point 16

Point 15

Point 14

Point 1 3

Point 12

Point 11

Point 10

Poin t

1

Point

9

Point

8

Point

7

P oin t

6

Point

5

Point

4

Point

3

Point 21

P oin t

2 0

Point

2

Point 19

Point 18

P oint

17

Point 16

Point 15

Point 14

Point 13

Point 1 2

Point 11

Point 10

Point

1

0.0010

0.0005

0.0000

Points

Sam

ple

Range

_R=0.000359

UCL=0.000924

LCL=0


Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

P oin t

3

Poin

t 21

Poin

t 20

Point 2

Po in

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Po in

t 10

Point 1

Point 9

Point 8

P oint

7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

P oin t

6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Po in

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.11

0.09

0.07

Points

Sam

ple

Mean

__X=0.09606UCL=0.09643LCL=0.09570


Point 9

Point 8

Poi n

t 7

Poi n

t 6

Point 5

Point 4

Point 3

Point 2

1

Poi n

t 20

Point 2

Point 1

9

Point 1

8

Point 1

7

Poi n

t 16

Poi nt 1

5

Point 1

4

Point 1

3

Point 1

2

Poi n

t 11

Poi nt 1

0

Point 1

0.11

0.09

0.07

Points

Operator 3Operator 2Operator 1

0.11

0.09

0.07

Operator

Point 9

Point 8

Point 7

P oin t 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

P oint

1

0.11

0.09

0.07

Points

Avera

ge Operator 1

Operator 2

Operator 3

Operator




Tolerance: ±.005


Components of Variation

R Chart by Operator

Xbar Chart by Operator

Z Location by Points

Z Location by Operator

Points * Operator Interaction

Gage R&R (ANOVA) for Z Location

·a ; I:- I ;. :

•

62


Gage R&R Study - ANOVA Method Gage R&R for Z Location

Gage name: CogniTens WLS 400M

Date of study: Nov 2011

Reported by: M. Hestness

Tolerance: ±.005


Two-Way ANOVA Table With Interaction Source DF SS MS F P

Points 20 0.0156222 0.0007811 18321.8 0.000

Operator 2 0.0000021 0.0000010 24.4 0.000

Points * Operator 40 0.0000017 0.0000000 0.9 0.716

Repeatability 126 0.0000063 0.0000001

Total 188 0.0156323

Alpha to remove interaction term = 0.25

Two-Way ANOVA Table Without Interaction Source DF SS MS F P

Points 20 0.0156222 0.0007811 16178.4 0.000

Operator 2 0.0000021 0.0000010 21.5 0.000

Repeatability 166 0.0000080 0.0000000

Total 188 0.0156323

Gage R&R %Contribution

Source VarComp (of VarComp)

Total Gage R&R 0.0000001 0.07

Repeatability 0.0000000 0.06

Reproducibility 0.0000000 0.02

Operator 0.0000000 0.02

Part-To-Part 0.0000868 99.93

Total Variation 0.0000868 100.00

Process tolerance = 0.01

Study Var %Study Var %Tolerance

Source StdDev (SD) (6 * SD) (%SV) (SV/Toler)

Total Gage R&R 0.0002530 0.0015180 2.71 15.18

Repeatability 0.0002197 0.0013184 2.36 13.18

Reproducibility 0.0001254 0.0007524 1.35 7.52

Operator 0.0001254 0.0007524 1.35 7.52

Part-To-Part 0.0093158 0.0558949 99.96 558.95

Total Variation 0.0093193 0.0559155 100.00 559.16

Number of Distinct Categories = 51

63


ATOS Triple Scan Input Data



0.1024 0.10486 0.10481 0.10482 0.10471 0.10472 0.10455 0.10494 0.10493 0.10482

0.0952 0.09681 0.09672 0.09684 0.09673 0.09678 0.09663 0.09689 0.09687 0.09681

0.0933 0.09444 0.09449 0.09441 0.09443 0.09442 0.09429 0.09443 0.09441 0.0944

0.0930 0.09394 0.09387 0.09374 0.09387 0.09392 0.09389 0.09389 0.09387 0.09386

0.0928 0.09340 0.09339 0.09327 0.09351 0.09338 0.09333 0.09338 0.09342 0.09339

0.0929 0.09308 0.09290 0.09275 0.09296 0.09295 0.09298 0.09296 0.09296 0.09303

0.0950 0.09482 0.09464 0.09449 0.09463 0.09455 0.09452 0.09451 0.09455 0.09466

0.0946 0.09573 0.09576 0.09562 0.09555 0.09565 0.09546 0.09568 0.0957 0.09555

0.0997 0.10029 0.10038 0.10028 0.10024 0.10027 0.10019 0.10022 0.10022 0.10012

0.1027 0.10303 0.10314 0.10295 0.10303 0.10301 0.10303 0.103 0.103 0.10295

0.1020 0.10221 0.10228 0.10202 0.10222 0.10232 0.10222 0.10223 0.10222 0.10227

0.0976 0.09744 0.09726 0.09721 0.09739 0.09732 0.09731 0.09737 0.09733 0.09739

0.0897 0.08960 0.08950 0.08916 0.08938 0.08945 0.08942 0.08933 0.08932 0.08941

0.0821 0.08177 0.08151 0.08138 0.08157 0.08157 0.08144 0.0815 0.08148 0.08166

0.1012 0.10173 0.10190 0.10179 0.10173 0.10175 0.10156 0.10175 0.10171 0.10158

0.1093 0.10926 0.10925 0.10912 0.10919 0.10924 0.1091 0.1092 0.10919 0.10907

0.1120 0.11161 0.11142 0.11146 0.11142 0.1116 0.11143 0.11159 0.1115 0.11151

0.1080 0.10753 0.10734 0.10713 0.10739 0.10751 0.10738 0.10737 0.10731 0.10732

0.0977 0.09701 0.09703 0.09694 0.09724 0.09722 0.09717 0.0971 0.09707 0.09714

0.0848 0.08365 0.08351 0.08321 0.08351 0.08359 0.0835 0.08338 0.08337 0.0835

0.0735 0.07230 0.07203 0.07171 0.07206 0.0722 0.0722 0.07198 0.07195 0.07216

64


0.110.100.090.080.07

0.003

0.002

0.001

0.000

-0.001

-0.002

Reference Value

Bia

s

0

Regression

95% CI

Data

Avg Bias

BiasLinearity

3.0

1.5

0.0

Pe

rce

nt

C onstant -0.0028433 0.0006958 0.000

Slope 0.031101 0.007205 0.000


Gage Linearity

S 0.0008776 R-Sq 9.1%


A v erage 0.0001476 0.3 0.000

0.0735 -0.0014344 2.5 0.000

0.0821 -0.0005578 1.0 0.000

0.0848 -0.0013311 2.4 0.000

0.0897 -0.0003033 0.5 0.000

0.0928 0.0005856 1.0 0.000

0.0929 0.0000522 0.1 0.187

0.093 0.0008722 1.5 0.000


Gage Bias

Gage name: A TO S Triple Scan



Tolerance: ±.005





500

250

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Poin t

8

Point

7

Point

6

P oint

5

Point

4

Point

3

Point 21

P oin t

20

Point

2

Point 19

Point 18

Point 17

Point 16

Point 15

P oin t

1 4

Point 13

Point 12

Point 11

Point 10

Point

1

Point

9

Point

8

Point

7

Point

6

Poin t

5

Point

4

Point

3

Point 21

Point 2 0

Point

2

Point 19

P oin t

18

Point 17

Point 16

Point 15

Point 14

Point 13

Point 12

P oin t

1 1

Point 10

Point

1

P oin t

9

Point

8

Point

7

Point

6

Point

5

Point

4

Point

3

P oint

21

Point 20

Point

2

Point 19

Point 1 8

Point 17

Point 16

P oin t

15

Point 14

Point 13

Point 12

Point 11

Point 10

Point

1

0.00050

0.00025

0.00000

Points

Sam

ple

Range

_R=0.0001510

UCL=0.0003886

LCL=0


Point 9

P oin t

8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Po in

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Po in

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Po in

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

P oin t

9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Po in

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Po in

t 12

Poin

t 11

Poin

t 10

Point 1

0.11

0.09

0.07

Points

Sam

ple

Mean

__X=0.09631UCL=0.09647LCL=0.09616


Point 9

Point 8

Poi n

t 7

Poi n

t 6

Point 5

Point 4

Point 3

Point 2

1

Poi n

t 20

Point 2

Point 1

9

Point 1

8

Point 1

7

Poi n

t 16

Poi nt 1

5

Point 1

4

Point 1

3

Point 1

2

Poi n

t 11

Poi nt 1

0

Point 1

0.11

0.09

0.07

Points


0.11

0.09

0.07

Operator

Point 9

Point 8

Point 7

P oin t 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

P oint

1

0.11

0.09

0.07

Points

Avera

ge Operator 1

Operator 2

Operator 3

Operator




Tolerance: ±.005



R Chart by Operator






I •

1 . J l l~ I . . _ EZ:SL:::::I I

~I

I! ! !I tv\r-1D

65



Gage name: ATOS Triple Scan



Tolerance: ±.005



Points 20 0.0159177 0.0007959 73536.4 0.000

Operator 2 0.0000000 0.0000000 0.0 0.975

Points * Operator 40 0.0000004 0.0000000 1.1 0.285

Repeatability 126 0.0000012 0.0000000

Total 188 0.0159193



Points 20 0.0159177 0.0007959 81253.4 0.000

Operator 2 0.0000000 0.0000000 0.0 0.973

Repeatability 166 0.0000016 0.0000000

Total 188 0.0159193



Total Gage R&R 0.0000000 0.01



Operator 0.0000000 0.00

Part-To-Part 0.0000884 99.99





Total Gage R&R 0.0000990 0.0005938 1.05 5.94

Repeatability 0.0000990 0.0005938 1.05 5.94


Operator 0.0000000 0.0000000 0.00 0.00

Part-To-Part 0.0094038 0.0564226 99.99 564.23

Total Variation 0.0094043 0.0564257 100.00 564.26


66


Rexcan 4 White Light Scanner Input Data



0.1024 0.10584 0.10367 0.10459 0.10485 0.10523 0.10419 0.10461 0.10473 0.10504

0.0952 0.09663 0.09694 0.0957 0.09679 0.09697 0.09635 0.09535 0.09567 0.09546

0.0933 0.09531 0.09493 0.09314 0.09474 0.09439 0.09534 0.09414 0.0944 0.09318

0.0930 0.09549 0.09431 0.09455 0.09374 0.09438 0.09617 0.09386 0.09339 0.09374

0.0928 0.09311 0.09258 0.09343 0.09383 0.09466 0.09366 0.09304 0.09307 0.09422

0.0929 0.09319 0.09289 0.09265 0.09329 0.09269 0.09376 0.09334 0.09243 0.09315

0.0950 0.09521 0.09758 0.09715 0.0959 0.09611 0.09657 0.09472 0.09536 0.09609

0.0946 0.09587 0.09723 0.09543 0.09589 0.09725 0.09646 0.09624 0.09498 0.09728

0.0997 0.10198 0.10023 0.10052 0.10058 0.10029 0.09976 0.09906 0.09887 0.09936

0.1027 0.10306 0.10484 0.1035 0.10253 0.10265 0.1026 0.10367 0.1053 0.10415

0.1020 0.10241 0.10090 0.10032 0.10135 0.10157 0.10012 0.10287 0.10077 0.10187

0.0976 0.09689 0.09814 0.0971 0.09821 0.0964 0.09579 0.09683 0.09798 0.09836

0.0897 0.09109 0.09091 0.08946 0.09147 0.09005 0.08938 0.08861 0.09004 0.09077

0.0821 0.08236 0.08092 0.08083 0.08406 0.08156 0.08365 0.08161 0.08111 0.08221

0.1012 0.10287 0.10142 0.10263 0.10017 0.09902 0.10204 0.09981 0.10064 0.1019

0.1093 0.10935 0.10780 0.10888 0.10914 0.10938 0.10751 0.10932 0.1091 0.10974

0.1120 0.11077 0.11071 0.11262 0.11064 0.1088 0.10918 0.11137 0.11036 0.11182

0.1080 0.10782 0.10659 0.10819 0.10719 0.10603 0.10666 0.10739 0.10715 0.10651

0.0977 0.09702 0.09778 0.09778 0.09802 0.09783 0.09648 0.09827 0.09701 0.09747

0.0848 0.08364 0.08287 0.08398 0.08486 0.08354 0.0832 0.08343 0.08353 0.08472

0.0735 0.07260 0.07343 0.07298 0.07628 0.07159 0.07136 0.07306 0.07237 0.07391

67


0.110.100.090.080.07

0.003

0.002

0.001

0.000

-0.001

-0.002

-0.003

-0.004

Reference Value

Bia

s 0

Regression

95% CI

Data

Avg Bias

BiasLinearity

1.0

0.5

0.0

Pe

rce

nt

C onstant 0.001189 0.001009 0.240

Slope -0.00945 0.01045 0.367


Gage Linearity

S 0.0012727 R-Sq 0.4%


A v erage 0.0002808 0.5 0.000

0.0735 -0.0004356 0.8 0.441

0.0821 -0.0000656 0.1 0.857

0.0848 -0.0010478 1.9 0.002

0.0897 0.0004978 0.9 0.152

0.0928 0.0007111 1.3 0.016

0.0929 0.0001433 0.3 0.353

0.093 0.0014033 2.6 0.002


Gage Bias

Gage name: Rexcan 4 White Light Scanner



Tolerance: ±.005





400

200

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

Poin t

9

Point

8

Point

7

Point

6

Point

5

Point

4

Poin t

3

Point 2 1

Point 20

Point

2

Point 19

Point 18

P oint

17

Point 1 6

Point 15

Point 14

Point 13

Point 12

P oint

11

Point 1 0

Point

1

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

0.004

0.002

0.000

Points

Sam

ple

Range

_R=0.001468

UCL=0.003779

LCL=0


Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.11

0.09

0.07

Points

Sam

ple

Mean

__X=0.09645UCL=0.09795LCL=0.09495


Point 9

Point 8

Poi n

t 7

Poi n

t 6

Point 5

Point 4

Point 3

Point 2

1

Poi n

t 20

Point 2

Point 1

9

Point 1

8

Point 1

7

Poi n

t 16

Poi nt 1

5

Point 1

4

Point 1

3

Point 1

2

Poi n

t 11

Poi nt 1

0

Point 1

0.11

0.09

0.07

Points


0.11

0.09

0.07

Operator

Point 9

Point 8

Point 7

P oin t 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

P oint

1

0.11

0.09

0.07

Points

Avera

ge Operator 1

Operator 2

Operator 3

Operator




Tolerance: ±.005



R Chart by Operator






• •

• •

D

I ~ ~ o= JIL::.....--....11~ I r::::z:::s/:::::1

~I

I! t !I r~o

68






Tolerance: ±.005



Points 20 0.0147228 0.0007361 705.552 0.000

Operator 2 0.0000018 0.0000009 0.881 0.422

Points * Operator 40 0.0000417 0.0000010 1.376 0.094

Repeatability 126 0.0000956 0.0000008

Total 188 0.0148619




Total Gage R&R 0.0000009 1.03



Operator 0.0000000 0.00

Operator*Points 0.0000001 0.12

Part-To-Part 0.0000817 98.97





Total Gage R&R 0.0009238 0.0055427 10.17 55.43

Repeatability 0.0008708 0.0052251 9.59 52.25


Operator 0.0000000 0.0000000 0.00 0.00

Operator*Points 0.0003082 0.0018492 3.39 18.49

Part-To-Part 0.0090375 0.0542253 99.48 542.25

Total Variation 0.0090846 0.0545078 100.00 545.08


69


Appendix G: Input Data and Results for Spot Face Panel




-0.0006 -0.0003 -0.0012 -0.0009 -0.0023 0.0006 -0.0009 -0.0007 -0.0021 0.0012 -0.0011 -0.0006 -0.0008 -0.0005 -0.0012 -0.0005 -0.0014 -0.0011 0.0000 -0.0017 -0.0037 -0.0041 -0.0028 -0.0028 -0.0041 -0.0052 -0.0031 -0.0030 -0.0033 -0.0030 -0.0077 -0.0079 -0.0071 -0.0070 -0.0072 -0.0074 -0.0080 -0.0067 -0.0065 -0.0058 -0.0115 -0.0108 -0.0111 -0.0110 -0.0118 -0.0113 -0.0117 -0.0112 -0.0126 -0.0112 -0.0155 -0.0146 -0.0146 -0.0150 -0.0160 -0.0148 -0.0143 -0.0161 -0.0157 -0.0156 -0.0194 -0.0196 -0.0189 -0.0190 -0.0201 -0.0196 -0.0192 -0.0200 -0.0193 -0.0197 -0.0009 -0.0018 -0.0012 -0.0009 -0.0007 -0.0010 -0.0009 -0.0014 -0.0017 -0.0009 -0.0016 -0.0017 -0.0014 -0.0024 -0.0021 -0.0023 -0.0021 -0.0014 -0.0018 -0.0014 -0.0035 -0.0033 -0.0040 -0.0039 -0.0045 -0.0052 -0.0039 -0.0030 -0.0037 -0.0035 -0.0076 -0.0078 -0.0075 -0.0079 -0.0069 -0.0069 -0.0073 -0.0076 -0.0074 -0.0072 -0.0107 -0.0106 -0.0110 -0.0112 -0.0107 -0.0109 -0.0102 -0.0112 -0.0105 -0.0103 -0.0156 -0.0157 -0.0155 -0.0159 -0.0160 -0.0146 -0.0159 -0.0156 -0.0156 -0.0157 -0.0194 -0.0204 -0.0194 -0.0189 -0.0194 -0.0187 -0.0192 -0.0184 -0.0190 -0.0198 -0.0014 -0.0011 -0.0013 -0.0011 -0.0015 -0.0016 -0.0013 -0.0014 -0.0010 -0.0013 -0.0019 -0.0016 -0.0014 -0.0018 -0.0017 -0.0016 -0.0015 -0.0017 -0.0015 -0.0013 -0.0034 -0.0032 -0.0034 -0.0034 -0.0034 -0.0036 -0.0034 -0.0031 -0.0032 -0.0035 -0.0075 -0.0075 -0.0077 -0.0074 -0.0078 -0.0077 -0.0074 -0.0076 -0.0073 -0.0078 -0.0118 -0.0115 -0.0113 -0.0117 -0.0117 -0.0114 -0.0116 -0.0117 -0.0117 -0.0117 -0.0158 -0.0157 -0.0156 -0.0155 -0.0155 -0.0157 -0.0156 -0.0156 -0.0155 -0.0157 -0.0190 -0.0186 -0.0188 -0.0188 -0.0189 -0.0189 -0.0187 -0.0188 -0.0192 -0.0190

70


0.000-0.005-0.010-0.015-0.020

0.002

0.001

0.000

-0.001

-0.002

Reference Value

Bia

s

0

Regression

95% CI

Data

Avg Bias

BiasLinearity

0.8

0.4

0.0

Pe

rce

nt

C onstant 0.00002382 0.00006249 0.704

Slope -0.007155 0.005781 0.217


Gage Linearity

S 0.0005253 R-Sq 0.8%


A v erage 0.0000850 0.2 0.010

-0.019425 -0.0000552 0.1 0.668

-0.0194 0.0001507 0.4 0.512

-0.019 0.0001712 0.4 0.028

-0.015775 0.0001752 0.4 0.001

-0.015625 -0.0000030 0.0 0.985

-0.01545 0.0002840 0.7 0.189

-0.0118 0.0002201 0.5 0.004


Gage Bias




Tolerance: -.020 to .000

Misc: Spot F ace Panel




200

100

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Point

6

Point

5

Point

4

Poin t

3

Point 21

Point 20

Point

2

Point 19

Point 1 8

Point 17

Point 16

Point 15

P oin t

14

Point 13

Point 12

Point 11

Point 10

Point

1

Point

9

Point

8

P oint

7

Point

6

Point

5

Point

4

Point

3

P oin t

21

Point 20

Point

2

Point 19

Point 18

Point 1 7

Point 16

Point 15

Point 14

Point 1 3

Point 12

Point 11

Point 10

Poin t

1

Point

9

Point

8

Point

7

P oin t

6

Point

5

Point

4

Point

3

Point 21

P oin t

2 0

Point

2

Point 19

Point 18

P oint

17

Point 16

Point 15

Point 14

Point 13

Point 1 2

Point 11

Point 10

Point

1

0.0030

0.0015

0.0000

Points

Sam

ple

Range

_R=0.000714

UCL=0.001838

LCL=0


Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

P oin t

3

Poin

t 21

Poin

t 20

Point 2

Po in

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Po in

t 10

Point 1

Point 9

Point 8

P oint

7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

P oin t

6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Po in

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points

Sam

ple

Mean

__X=-0.00847UCL=-0.00774LCL=-0.00920


Point 9

Point 8

Point 7

Poin t 6

Point

5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Po in

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points


0.00

-0.01

-0.02

Operator

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points

Avera

ge Operator 1

Operator 2

Operator 3

Operator







R Chart by Operator


Spot Face Depth by Points

Spot Face Depth by Operator


Gage R&R (ANOVA) for Spot Face Depth

.. D • • • • • • I • I

• • • I ' . . -· .. t=t--~F-t-+4 • • • ' !I • •• • • • •

• • • •

I = = . ~ ] 1~ I 1Sl:3/Sll

li*e~l 1 ~ ~ $ 1

1~1 ~D

71


Gage R&R Study - ANOVA Method Gage R&R for Spot Face Depth





Misc: Spot Face Panel


Points 20 0.0081490 0.0004075 1331.76 0.000

Operator 2 0.0000007 0.0000003 1.13 0.333

Points * Operator 40 0.0000122 0.0000003 1.29 0.148

Repeatability 126 0.0000300 0.0000002

Total 188 0.0081919




Total Gage R&R 0.0000003 0.57



Operator 0.0000000 0.00


Part-To-Part 0.0000452 99.43





Total Gage R&R 0.0005110 0.0030659 7.58 15.33

Repeatability 0.0004876 0.0029255 7.23 14.63


Operator 0.0000252 0.0001510 0.37 0.75

Operator*Points 0.0001508 0.0009048 2.24 4.52

Part-To-Part 0.0067260 0.0403557 99.71 201.78

Total Variation 0.0067453 0.0404720 100.00 202.36


72





-0.0006 -0.0006 -0.0006 -0.0007 -0.0006 -0.0005 -0.0006 -0.0006 -0.0007 -0.0007 -0.0011 -0.0013 -0.0014 -0.0011 -0.0012 -0.001 -0.0012 -0.0012 -0.0012 -0.0011 -0.0037 -0.0043 -0.0044 -0.0039 -0.0039 -0.004 -0.0038 -0.0037 -0.0036 -0.0038 -0.0077 -0.0075 -0.0075 -0.0079 -0.0079 -0.0076 -0.0078 -0.0074 -0.0074 -0.0075 -0.0115 -0.0113 -0.0117 -0.012 -0.012 -0.0125 -0.0116 -0.0114 -0.0116 -0.0115 -0.0155 -0.015 -0.0184 -0.0167 -0.0156 -0.0157 -0.0156 -0.0156 -0.0155 -0.0153 -0.0194 -0.0206 -0.0201 -0.0204 -0.0199 -0.0199 -0.0197 -0.0201 -0.0199 -0.0201 -0.0009 -0.0008 -0.0007 -0.0007 -0.0009 -0.0007 -0.0009 -0.0008 -0.0008 -0.0008 -0.0016 -0.0017 -0.0021 -0.0018 -0.002 -0.0019 -0.002 -0.0018 -0.0018 -0.0018 -0.0035 -0.0042 -0.004 -0.0048 -0.0037 -0.0038 -0.0038 -0.0038 -0.0037 -0.0038 -0.0076 -0.0078 -0.008 -0.0079 -0.0079 -0.0078 -0.0077 -0.0077 -0.0077 -0.0077 -0.0107 -0.0107 -0.011 -0.0115 -0.0107 -0.0107 -0.0108 -0.0107 -0.0107 -0.0107 -0.0156 -0.0157 -0.0163 -0.0157 -0.0154 -0.0155 -0.0156 -0.0156 -0.0156 -0.0156 -0.0194 -0.02 -0.0202 -0.0199 -0.0196 -0.0197 -0.0196 -0.0195 -0.0187 -0.0168 -0.0014 -0.0012 -0.0011 -0.0012 -0.0011 -0.0011 -0.0011 -0.0011 -0.0011 -0.0011 -0.0019 -0.0016 -0.0016 -0.0016 -0.0015 -0.0015 -0.0015 -0.0015 -0.0014 -0.0015 -0.0034 -0.0035 -0.0033 -0.0034 -0.0034 -0.0034 -0.0034 -0.0034 -0.0034 -0.0034 -0.0075 -0.0076 -0.0076 -0.0076 -0.0076 -0.0076 -0.0076 -0.0076 -0.0076 -0.0076 -0.0118 -0.0119 -0.0118 -0.0118 -0.0118 -0.0118 -0.0118 -0.0119 -0.0118 -0.0118 -0.0158 -0.0158 -0.0158 -0.0158 -0.0157 -0.0158 -0.0158 -0.0157 -0.0158 -0.0158 -0.0190 -0.0192 -0.0192 -0.0192 -0.0192 -0.0192 -0.0192 -0.0191 -0.0193 -0.0192

73


0.000-0.005-0.010-0.015-0.020

0.003

0.002

0.001

0.000

-0.001

-0.002

-0.003

Reference Value

Bia

s 0

Regression

95% CI

Data

Avg Bias

BiasLinearity

1.0

0.5

0.0

Pe

rce

nt

C onstant 0.00001275 0.00004894 0.795

Slope 0.013458 0.004527 0.003


Gage Linearity

S 0.0004114 R-Sq 4.5%


A v erage -0.0001024 0.2 0.000

-0.019425 -0.0006528 1.6 0.000

-0.0194 0.0000667 0.2 0.861

-0.019 -0.0002000 0.5 0.000

-0.015775 -0.0000028 0.0 0.805

-0.015625 -0.0000417 0.1 0.682

-0.01545 -0.0004833 1.2 0.231

-0.0118 -0.0000222 0.1 0.079


Gage Bias









200

100

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Point

6

Point

5

Point

4

Poin t

3

Point 21

Point 20

Point

2

Point 19

Point 1 8

Point 17

Point 16

Point 15

P oin t

14

Point 13

Point 12

Point 11

Point 10

Point

1

Point

9

Point

8

P oint

7

Point

6

Point

5

Point

4

Point

3

P oin t

21

Point 20

Point

2

Point 19

Point 18

Point 1 7

Point 16

Point 15

Point 14

Point 1 3

Point 12

Point 11

Point 10

Poin t

1

Point

9

Point

8

Point

7

P oin t

6

Point

5

Point

4

Point

3

Point 21

P oin t

2 0

Point

2

Point 19

Point 18

P oint

17

Point 16

Point 15

Point 14

Point 13

Point 1 2

Point 11

Point 10

Point

1

0.0030

0.0015

0.0000

Points

Sam

ple

Range

_R=0.000271UCL=0.000699LCL=0


Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

P oin t

3

Poin

t 21

Poin

t 20

Point 2

Po in

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Po in

t 10

Point 1

Point 9

Point 8

P oint

7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

P oin t

6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Po in

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points

Sam

ple

Mean

__X=-0.00866UCL=-0.00838LCL=-0.00893


Point 9

Point 8

Point 7

Poin t 6

Point

5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Po in

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points


0.00

-0.01

-0.02

Operator

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points

Avera

ge Operator 1

Operator 2

Operator 3

Operator







R Chart by Operator






• D •

t=i=-A~<::4-- ·~ I : · 11 • • • • • • •

•

I .= .= . ~ 1 1~ I I'SL3/S[I

1~1

74









Points 20 0.0084910 0.0004246 2197.22 0.000

Operator 2 0.0000025 0.0000012 6.42 0.004

Points * Operator 40 0.0000077 0.0000002 1.99 0.002

Repeatability 126 0.0000122 0.0000001

Total 188 0.0085135




Total Gage R&R 0.0000001 0.31



Operator 0.0000000 0.04


Part-To-Part 0.0000472 99.69





Total Gage R&R 0.0003817 0.0022905 5.55 11.45

Repeatability 0.0003115 0.0018690 4.53 9.35


Operator 0.0001290 0.0007738 1.88 3.87

Operator*Points 0.0001791 0.0010743 2.60 5.37

Part-To-Part 0.0068667 0.0412000 99.85 206.00

Total Variation 0.0068773 0.0412636 100.00 206.32


75





-0.0006 -0.0006 -0.0003 -0.0002 -0.0008 -0.0003 -0.0003 -0.0006 -0.0004 -0.0009 -0.0011 -0.0015 -0.0015 -0.0004 -0.0015 -0.0017 -0.0014 -0.0014 -0.0004 -0.0011 -0.0037 -0.0041 -0.0047 -0.0026 -0.0039 -0.0037 -0.0041 -0.0035 -0.0012 -0.0039 -0.0077 -0.0078 -0.0075 -0.0025 -0.0074 -0.0073 -0.0075 -0.0073 -0.0018 -0.008 -0.0115 -0.0118 -0.0116 -0.0041 -0.0113 -0.0114 -0.0113 -0.0121 -0.0037 -0.0117 -0.0155 -0.015 -0.015 -0.007 -0.014 -0.0135 -0.0136 -0.013 -0.0029 -0.0151 -0.0194 -0.0197 -0.0198 -0.0074 -0.0202 -0.02 -0.0193 -0.0198 -0.0085 -0.0196 -0.0009 -0.0013 -0.0001 -0.0003 -0.0012 -0.001 -0.0003 -0.001 -0.0014 -0.0021 -0.0016 -0.0019 -0.002 -0.0005 -0.0014 -0.0014 -0.0017 -0.0019 0 -0.0025 -0.0035 -0.0038 -0.0041 -0.0001 -0.0039 -0.0036 -0.0039 -0.0037 -0.0009 -0.0045 -0.0076 -0.0081 -0.008 -0.0003 -0.0078 -0.0074 -0.0075 -0.0076 -0.001 -0.0083 -0.0107 -0.0108 -0.01 -0.0039 -0.0107 -0.0111 -0.0106 -0.0108 -0.0012 -0.0111 -0.0156 -0.0154 -0.0153 -0.0002 -0.0161 -0.0152 -0.0158 -0.0141 -0.0012 -0.0158 -0.0194 -0.02 -0.0197 -0.0065 -0.0196 -0.0193 -0.0202 -0.019 -0.0023 -0.0189 -0.0014 -0.0012 -0.0013 -0.0014 -0.0015 -0.002 -0.0024 -0.0022 -0.0009 -0.0012 -0.0019 -0.0016 -0.0015 -0.0014 -0.0015 -0.0017 -0.0017 -0.0015 -0.0018 -0.0015 -0.0034 -0.0034 -0.0035 -0.0035 -0.0032 -0.0035 -0.0036 -0.0036 -0.0033 -0.0033 -0.0075 -0.0106 -0.0078 -0.0065 -0.009 -0.0084 -0.0104 -0.009 -0.0079 -0.0075 -0.0118 -0.0118 -0.0117 -0.0119 -0.0119 -0.0117 -0.0119 -0.0119 -0.0118 -0.0118 -0.0158 -0.0157 -0.0156 -0.0156 -0.0156 -0.0159 -0.0159 -0.0152 -0.0159 -0.0157 -0.0190 -0.0186 -0.0192 -0.0193 -0.0192 -0.0192 -0.0191 -0.0191 -0.019 -0.019

76


0.000-0.005-0.010-0.015-0.020

0.020

0.015

0.010

0.005

0.000

Reference Value

Bia

s

0

Regression

95% CI

Data

Avg Bias

BiasLinearity

10

5

0

Pe

rce

nt

C onstant -0.0001664 0.0003524 0.637

Slope -0.12061 0.03260 0.000


Gage Linearity

S 0.0029625 R-Sq 6.8%


A v erage 0.0008653 2.2 0.000

-0.019425 0.0022806 5.7 0.144

-0.0194 0.0032333 8.1 0.139

-0.019 -0.0000778 0.2 0.339

-0.015775 0.0000972 0.2 0.241

-0.015625 0.0035028 8.8 0.081

-0.01545 0.0033278 8.3 0.040

-0.0118 -0.0000222 0.1 0.339


Gage Bias

Gage name: Rexcan 4 White Light Sy stem








200

100

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

Poin t

9

Point

8

Point

7

Point

6

Point

5

Point

4

Poin t

3

Point 2 1

Point 20

Point

2

Point 19

Point 18

P oint

17

Point 1 6

Point 15

Point 14

Point 13

Point 12

P oint

11

Point 1 0

Point

1

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

0.016

0.008

0.000

Points

Sam

ple

Range

_R=0.00327

UCL=0.00843

LCL=0


Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points

Sam

ple

Mean

__X=-0.00769UCL=-0.00434

LCL=-0.01104


Point 9

Point 8

Point 7

Poin t 6

Point

5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Po in

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points


0.00

-0.01

-0.02

Operator

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.00

-0.01

-0.02

Points

Avera

ge Operator 1

Operator 2

Operator 3

Operator




Tolerance: -020 to .000



R Chart by Operator






77






Tolerance: -020 to .000



Points 20 0.0065752 0.0003288 98.2336 0.000

Operator 2 0.0000841 0.0000420 12.5591 0.000

Points * Operator 40 0.0001339 0.0000033 0.3416 1.000

Repeatability 126 0.0012343 0.0000098

Total 188 0.0080275



Points 20 0.0065752 0.0003288 39.8891 0.000

Operator 2 0.0000841 0.0000420 5.0998 0.007

Repeatability 166 0.0013682 0.0000082

Total 188 0.0080275



Total Gage R&R 0.0000088 19.77



Operator 0.0000005 1.21

Part-To-Part 0.0000356 80.23





Total Gage R&R 0.0029628 0.0177769 44.47 88.88

Repeatability 0.0028709 0.0172252 43.09 86.13


Operator 0.0007324 0.0043942 10.99 21.97

Part-To-Part 0.0059677 0.0358061 89.57 179.03

Total Variation 0.0066627 0.0399762 100.00 199.88


78


Appendix H: Input Data and Results for Countersink Panel



Nominal Diam. Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3

0.3764 0.3903 0.3878 0.3850 0.3846 0.3901 0.3886 0.3867 0.3920 0.3889

0.507 0.4455 0.4465 0.4441 0.4431 0.4465 0.4457 0.4462 0.4467 0.4475

0.5781 0.5742 0.5740 0.5720 0.5701 0.5744 0.5739 0.5733 0.5739 0.5752

0.401 0.3985 0.3971 0.3956 0.3944 0.3983 0.3965 0.3944 0.3972 0.3976

0.5286 0.5246 0.5237 0.5205 0.5222 0.5242 0.5208 0.5190 0.5216 0.5234

0.6403 0.6492 0.6490 0.6462 0.6448 0.6492 0.6451 0.6454 0.6472 0.6474

0.7778 0.7777 0.7790 0.7755 0.7741 0.7767 0.7735 0.7772 0.7760 0.7730

0.3709 0.3807 0.3780 0.3780 0.3806 0.3810 0.3806 0.3793 0.3817 0.3812

0.4443 0.4484 0.4457 0.4461 0.4466 0.4453 0.4465 0.4457 0.4443 0.4423

0.5726 0.6444 0.6431 0.6434 0.6428 0.6425 0.6431 0.6436 0.6449 0.6440

0.3954 0.3935 0.3815 0.3908 0.3905 0.3897 0.3890 0.3872 0.3906 0.3909

0.5199 0.5262 0.5235 0.5221 0.5223 0.5223 0.5210 0.5224 0.5231 0.5134

0.6399 0.6486 0.6464 0.6439 0.6454 0.6462 0.6440 0.6465 0.6447 0.6463

0.7756 0.7819 0.7776 0.7770 0.7778 0.7767 0.7743 0.7802 0.7783 0.7770

0.374 0.3706 0.3651 0.3681 0.3697 0.3685 0.3675 0.3675 0.3688 0.3709

0.4495 0.4407 0.4367 0.4375 0.4373 0.4351 0.4376 0.4359 0.4389 0.4381

0.5692 0.6420 0.6385 0.6388 0.6395 0.6358 0.6374 0.6381 0.6397 0.6385

0.396 0.3871 0.3820 0.3828 0.3840 0.3824 0.3815 0.3826 0.3841 0.3826

0.5218 0.5175 0.5215 0.5151 0.5209 0.5188 0.5192 0.5195 0.5220 0.5204

0.6464 0.6478 0.6463 0.6460 0.6439 0.6436 0.6437 0.6440 0.6469 0.6444

0.7747 0.7820 0.7766 0.7698 0.7760 0.7677 0.7716 0.7707 0.7729 0.7737

79


0.80.70.60.50.4

0.075

0.050

0.025

0.000

-0.025

-0.050

Reference Value

Bia

s

0

Regression

95% CI

Data

Avg Bias

BiasLinearity

2

1

0

Pe

rce

nt

C onstant -0.010525 0.007934 0.186

Slope 0.02495 0.01437 0.084


Gage Linearity

S 0.0260344 R-Sq 1.6%


A v erage 0.0028508 0.3 0.000

0.3709 0.0092222 1.1 0.000

0.374 -0.0054749 0.6 0.000

0.3764 0.0118382 1.4 0.000

0.3954 -0.0061149 0.7 0.002

0.396 -0.0127627 1.5 0.000

0.401 -0.0043691 0.5 0.000

0.4443 0.0013604 0.2 0.078


Gage Bias




Tolerance: ±.010

Misc: C ountersink Panel




4000

2000

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

Poin t

9

Point

8

Point

7

Point

6

Point

5

Point

4

Poin t

3

Point 2 1

Point 20

Point

2

Point 19

Point 18

P oint

17

Point 1 6

Point 15

Point 14

Point 13

Point 12

P oint

11

Point 1 0

Point

1

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

0.010

0.005

0.000

Points

Sam

ple

Range

_R=0.00350

UCL=0.00902

LCL=0


Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.8

0.6

0.4

Points

Sam

ple

Mean

__X=0.5390UCL=0.5426LCL=0.5354


Point 9

Poi n

t 8

Point 7

Point 6

Poi nt 5

Point 4

Poi n

t 3

Poi nt 2

1

Point 2

0

Poi nt 2

Point 1

9

Point 1

8

Poi nt 1

7

Point 1

6

Poi n

t 15

Point 1

4

Point 1

3

Poi nt 1

2

Point 1

1

Poi n

t 10

Point 1

0.8

0.6

0.4

Points


0.8

0.6

0.4

Operator

Point 9

P oint

8

Point 7

Point 6

P oin t 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

P oint

1

0.8

0.6

0.4

Points

Avera

ge

Operator 1

Operator 2

Operator 3

Operator




Tolerance: ±.010

Misc: C ountersink Panel Point


R Chart by Operator


Countersink Diameter by Points

Countersink Diameter by Operator


Gage R&R (ANOVA) for Countersink Diameter

• D

I

1 m~ I 11'21&ZSZkl

I~ ~ ~I ~I lfZ)f&ZS1JD

80


Gage R&R Study - ANOVA Method Gage R&R for Countersink Diameter




Tolerance: ±.010

Misc: Countersink Panel Point


Points 20 3.57451 0.178725 50592.6 0.000

Operator 2 0.00003 0.000016 4.4 0.018

Points * Operator 40 0.00014 0.000004 0.7 0.859

Repeatability 126 0.00060 0.000005

Total 188 3.57528



Points 20 3.57451 0.178725 40100.8 0.000

Operator 2 0.00003 0.000016 3.5 0.032

Repeatability 166 0.00074 0.000004

Total 188 3.57528



Total Gage R&R 0.0000046 0.02



Operator 0.0000002 0.00

Part-To-Part 0.0198579 99.98





Total Gage R&R 0.002153 0.012917 1.53 64.58

Repeatability 0.002111 0.012667 1.50 63.33


Operator 0.000421 0.002528 0.30 12.64

Part-To-Part 0.140918 0.845508 99.99 4227.54

Total Variation 0.140934 0.845606 100.00 4228.03


81





0.3764 0.3689 0.3706 0.3695 0.375 0.3721 0.3714 0.3748 0.3744 0.3752

0.507 0.545 0.5476 0.5477 0.545 0.5468 0.5424 0.5433 0.5438 0.5444

0.5781 0.5709 0.5706 0.5705 0.5752 0.5752 0.5733 0.575 0.5748 0.5753

0.401 0.3895 0.3898 0.3884 0.3986 0.3969 0.39 0.3999 0.3992 0.4001

0.5286 0.5227 0.5229 0.5226 0.525 0.5248 0.5222 0.5268 0.5265 0.5264

0.6403 0.6448 0.6435 0.6444 0.6461 0.6455 0.6431 0.6448 0.6453 0.6444

0.7778 0.7738 0.7824 0.7798 0.7766 0.7772 0.7741 0.7763 0.776 0.7766

0.3709 0.3617 0.3647 0.3625 0.3679 0.3693 0.3671 0.3683 0.3677 0.3685

0.4443 0.4391 0.4383 0.4397 0.4438 0.4437 0.4418 0.4433 0.4427 0.4436

0.5726 0.5692 0.5686 0.5689 0.5712 0.5721 0.5711 0.572 0.5715 0.5724

0.3954 0.3857 0.3873 0.3877 0.392 0.3914 0.3932 0.3932 0.393 0.3934

0.5199 0.5155 0.5164 0.5163 0.518 0.5173 0.5167 0.5188 0.5186 0.5185

0.6399 0.6441 0.6438 0.6438 0.6469 0.6449 0.645 0.6453 0.6449 0.6458

0.7756 0.77 0.7687 0.7708 0.7773 0.7744 0.7735 0.7741 0.7742 0.7743

0.374 0.3561 0.3629 0.3612 0.3662 0.3659 0.3635 0.3633 0.3643 0.3633

0.4495 0.4451 0.445 0.4465 0.4474 0.4478 0.4493 0.4496 0.4495 0.4498

0.5692 0.5673 0.5659 0.5663 0.5674 0.5668 0.5673 0.569 0.5696 0.5694

0.396 0.385 0.3872 0.3871 0.3942 0.3943 0.3927 0.3945 0.3947 0.3947

0.5218 0.5174 0.5175 0.5179 0.5206 0.5187 0.5187 0.5213 0.5211 0.5211

0.6464 0.6505 0.6481 0.6498 0.6512 0.6511 0.6497 0.6501 0.65 0.6507

0.7747 0.775 0.7798 0.7779 0.777 0.7762 0.7754 0.7748 0.775 0.7753

82


0.80.70.60.50.4

0.04

0.03

0.02

0.01

0.00

-0.01

-0.02

Reference Value

Bia

s

0

Regression

95% CI

Data

Avg Bias

BiasLinearity

1.6

0.8

0.0

Pe

rce

nt

C onstant -0.008111 0.002879 0.005

Slope 0.014522 0.005214 0.006


Gage Linearity

S 0.0094474 R-Sq 4.0%


A v erage -0.0003249 0.0 0.026

0.3709 -0.0044889 0.5 0.001

0.374 -0.0110333 1.3 0.000

0.3764 -0.0039667 0.5 0.001

0.3954 -0.0046333 0.6 0.001

0.396 -0.0044000 0.5 0.004

0.401 -0.0062889 0.8 0.002

0.4443 -0.0025222 0.3 0.004


Gage Bias




Tolerance: ±.010





4000

2000

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

Poin t

9

Point

8

Point

7

Point

6

Point

5

Point

4

Poin t

3

Point 2 1

Point 20

Point

2

Point 19

Point 18

P oint

17

Point 1 6

Point 15

Point 14

Point 13

Point 12

P oint

11

Point 1 0

Point

1

Point

9

Point

8

Point

7

Poin t

6

Point

5

Point

4

Point

3

Point 21

Point 20

Poin t

2

Point 1 9

Point 18

Point 17

Point 16

Point 15

P oint

14

Point 1 3

Point 12

Point 11

Point 10

Point

1

0.008

0.004

0.000

Points

Sam

ple

Range

_R=0.001789

UCL=0.004605

LCL=0


Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.8

0.6

0.4

Points

Sam

ple

Mean

__X=0.5358UCL=0.5377LCL=0.5340


Point 9

Poi n

t 8

Point 7

Point 6

Poi nt 5

Point 4

Poi n

t 3

Poi nt 2

1

Point 2

0

Poi nt 2

Point 1

9

Point 1

8

Poi nt 1

7

Point 1

6

Poi n

t 15

Point 1

4

Point 1

3

Poi nt 1

2

Point 1

1

Poi n

t 10

Point 1

0.8

0.6

0.4

Points


0.8

0.6

0.4

Operator

Point 9

P oint

8

Point 7

Point 6

P oin t 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

P oint

1

0.8

0.6

0.4

Points

Avera

ge

Operator 1

Operator 2

Operator 3

Operator




Tolerance: ±.010



R Chart by Operator






I I:- I

•

1 ... m~ ~~~~

~I

83






Tolerance: ±.010

Misc: Countersink Panel


Points 20 3.39445 0.169722 16786.1 0.000

Operator 2 0.00035 0.000177 17.5 0.000

Points * Operator 40 0.00040 0.000010 5.9 0.000

Repeatability 126 0.00022 0.000002

Total 188 3.39542




Total Gage R&R 0.0000072 0.04



Operator 0.0000027 0.01


Part-To-Part 0.0188569 99.96





Total Gage R&R 0.002678 0.016066 1.95 80.33

Repeatability 0.001313 0.007876 0.96 39.38


Operator 0.001628 0.009768 1.19 48.84

Operator*Points 0.001672 0.010033 1.22 50.16

Part-To-Part 0.137320 0.823923 99.98 4119.61

Total Variation 0.137347 0.824079 100.00 4120.40


84





0.3764 0.3838 0.3726 0.3708 0.3808 0.3803 0.3803 0.3685 0.3829 0.373

0.507 0.5417 0.5401 0.5379 0.543 0.5446 0.5442 0.5497 0.5366 0.5426

0.5781 0.5715 0.569 0.572 0.5785 0.5753 0.5824 0.5733 0.5758 0.5746

0.401 0.4026 0.3979 0.4008 0.4018 0.4058 0.4075 0.4032 0.402 0.4041

0.5286 0.5342 0.5226 0.5285 0.5262 0.5343 0.5323 0.5274 0.5273 0.5256

0.6403 0.6389 0.6384 0.6365 0.6453 0.6473 0.6438 0.6334 0.6433 0.639

0.7778 0.7767 0.771 0.7772 0.7746 0.7864 0.779 0.7731 0.7756 0.7809

0.3709 0.3677 0.3656 0.3664 0.3755 0.3842 0.3779 0.3601 0.3738 0.3605

0.4443 0.4415 0.4387 0.4329 0.4547 0.4522 0.4662 0.4355 0.4493 0.4361

0.5726 0.57 0.5654 0.5661 0.5731 0.5787 0.5776 0.566 0.5741 0.571

0.3954 0.3923 0.391 0.3904 0.3946 0.3985 0.4002 0.3894 0.3944 0.3883

0.5199 0.5173 0.5158 0.515 0.5224 0.5229 0.5281 0.5157 0.5199 0.5143

0.6399 0.6409 0.6402 0.6384 0.6468 0.6522 0.653 0.6425 0.6492 0.6427

0.7756 0.7716 0.7683 0.7648 0.7761 0.7775 0.7832 0.7704 0.7767 0.7692

0.374 0.3595 0.3604 0.3662 0.368 0.3766 0.3744 0.3509 0.3646 0.356

0.4495 0.4573 0.4578 0.4557 0.4582 0.4648 0.4582 0.4444 0.4556 0.4449

0.5692 0.5675 0.573 0.5754 0.577 0.5761 0.5755 0.5653 0.5742 0.5621

0.396 0.3902 0.392 0.3975 0.3955 0.4006 0.4022 0.3893 0.3993 0.3875

0.5218 0.518 0.5229 0.5234 0.5236 0.5237 0.5274 0.5207 0.5272 0.5123

0.6464 0.6468 0.6503 0.6479 0.6499 0.6557 0.651 0.6456 0.651 0.6465

0.7747 0.7752 0.7777 0.7719 0.7754 0.78 0.7781 0.7722 0.7788 0.7756

85


0.80.70.60.50.4

0.05

0.04

0.03

0.02

0.01

0.00

-0.01

-0.02

Reference Value

Bia

s

0

Regression

95% CI

Data

Avg Bias

BiasLinearity

0.2

0.1

0.0

Pe

rce

nt

C onstant 0.000738 0.002970 0.804

Slope 0.001714 0.005379 0.750


Gage Linearity

S 0.0097462 R-Sq 0.1%


A v erage 0.0016571 0.2 0.000

0.3709 -0.0007111 0.1 0.793

0.374 -0.0099333 1.2 0.009

0.3764 0.0006000 0.1 0.728

0.3954 -0.0021667 0.3 0.137

0.396 -0.0011000 0.1 0.512

0.401 0.0018556 0.2 0.117

0.4443 0.0009333 0.1 0.803


Gage Bias

Gage name: Rexcan 4 White Light Sy stem



Tolerance: ±.010





4000

2000

0

Perc

ent

% Contribution

% Study Var

% Tolerance

Point

9

Point

8

Point

7

Point

6

Poin t

5

Point

4

Point

3

Point 21

Point 20

Point

2

Point 19

Point 18

P oin t

17

Point 1 6

Point 15

Point 14

Point 13

Point 12

Point 11

Point 10

Point

1

Point

9

Point

8

Point

7

Point

6

Point

5

P oint

4

Poin t

3

Point 2 1

Point 20

Point

2

Point 19

Point 18

Point 17

Point 16

P oin t

15

Point 1 4

Point 13

Point 12

Point 11

Point 10

Point

1

Poin t

9

Point

8

Point

7

Point

6

Point

5

Point

4

Point

3

Point 21

P oin t

20

Point

2

Point 19

Point 18

Point 17

Point 16

Point 15

Point 14

P oin t

1 3

Point 12

Point 11

Point 10

Point

1

0.02

0.01

0.00

Points

Sam

ple

Range

_R=0.00682

UCL=0.01756

LCL=0


Point 9

Point 8

Point 7

Point 6

P oin t

5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Po in

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Po in

t 10

Point 1

Point 9

Point 8

Point 7

Point 6

Point 5

P oint

4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Po in

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

P oin t

9

Point 8

Point 7

Point 6

Point 5

Point 4

Point 3

Po in

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Po in

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

Point 1

0.8

0.6

0.4

Points

Sam

ple

Mean

__X=0.5378UCL=0.5448LCL=0.5308


Point 9

Poi n

t 8

Point 7

Point 6

Poi nt 5

Point 4

Poi n

t 3

Poi nt 2

1

Point 2

0

Poi nt 2

Point 1

9

Point 1

8

Poi nt 1

7

Point 1

6

Poi n

t 15

Point 1

4

Point 1

3

Poi nt 1

2

Point 1

1

Poi n

t 10

Point 1

0.8

0.6

0.4

Points


0.8

0.6

0.4

Operator

Point 9

P oint

8

Point 7

Point 6

P oin t 5

Point 4

Point 3

Poin

t 21

Poin

t 20

Point 2

Poin

t 19

Poin

t 18

Poin

t 17

Poin

t 16

Poin

t 15

Poin

t 14

Poin

t 13

Poin

t 12

Poin

t 11

Poin

t 10

P oint

1

0.8

0.6

0.4

Points

Avera

ge

Operator 1

Operator 2

Operator 3

Operator




Tolerance: ±.010



R Chart by Operator






D

lj~ I

I$ $ $1 ~EJ

86






Tolerance: ±.010

Misc: Countersink Panel


Points 20 3.30650 0.165325 6759.36 0.000

Operator 2 0.00212 0.001061 43.40 0.000

Points * Operator 40 0.00098 0.000024 1.45 0.063

Repeatability 126 0.00213 0.000017

Total 188 3.31172




Total Gage R&R 0.0000359 0.19



Operator 0.0000165 0.09


Part-To-Part 0.0183667 99.81





Total Gage R&R 0.005988 0.035929 4.41 179.64

Repeatability 0.004107 0.024641 3.03 123.21


Operator 0.004057 0.024343 2.99 121.72

Operator*Points 0.001591 0.009545 1.17 47.73

Part-To-Part 0.135524 0.813143 99.90 4065.71

Total Variation 0.135656 0.813936 100.00 4069.68
