spc project report

A REPORT

ON

STATISTICAL PROCESS CONTROL

ON

ROTARY COMPRESSOR

BY

VISHWANI. M 04951A2144

ANAND BABU .D 04951A2103

SRINIVASAN .V.K 04951A2134

SRIKANTH.P 04951A0345

TECUMSEH INDIA PVT. LTD. (HYDERABAD)

A PROJECT WORK station of

INSTITUTE OF AERONAUTICAL ENGINEERING,DUNDIGAL

(JUNE, 2007)

ACKNOWLEDGEMENTS

Our experience in Tecumseh India Pvt. Ltd has been a wonderful exposure to

professional world of manufacturing. Firstly, I thank Tecumseh for giving us an

opportunity to work on this project. We are grateful to Prof. Harinath Prasad, HOD

(MECH), Prof. Subbaraju for giving us an opportunity to work in this organization.

We thank project mentors, Mr. Nagabushanam, Mr. Balaji chander, Mr.

Pradeep, Mr. Krishna Rao for their continuous feedback and suggestions for the

progress of the project. Thank Mr. Vishnu, Mr. Joseph, Mr. Naresh, Mr. Vivek, Mr.

Ravi, Mr. Sanjeevi, Mr. Rajesh and Mr. Naveen for their valuable co-operation and

suggestions in team work.

.

INSTITUTE OF AERONAUTICAL ENGINEERING,DUNDIGAL

Station: Tecumseh India Pvt. Ltd Centre: Hyderabad

Duration: 1 month Date of Start: June 23rd, 2007

Date of Submission: 26th July

Title of the project: Statistical process control

Students name(s): Vishwani. M (AERO)

D. Anand babu (AERO)

V.K. Srinivasan (AERO)

P. Srikanth (MECH).

Project Areas: PRODUCTION TECHNOLOGY

Abstract:

To improve the quality of a rotary compressor by optimizing various

parameters which contribute to performance of compressor, so as to minimize the

rejection rate. A set of process parameters responsible for the variation in

performance of compressor to be found out and to be optimized in order to control

the rejections by applying 7 QC Tools and SPC Techniques.

Signature(s) of student(s) Signature of PS Faculty

Date:

CONTENTS

Acknowledgements iii

Abstract iv

1. Introduction to Organization 1

2. Introduction to 7QC tools

3. statistical process control 6

3.1 Definition

3.2

1. Introduction to the Organization

Tecumseh Products Company is a full line independent global manufacturing

of hermit compressors for air conditioning and refrigeration products, gasoline

engines and power train components for lawn and garden application, and pumps.

Their products are cool in over 100 countries around the world.

The company has proposed by providing high quality competitively priced

product on an expanding global basis. They have a sixty-year history of growth

through successful development and application of new technologies and through

acquisitions.

Compressors products include a broad range of a conditioning and

refrigeration compressors and compressors parts as well as refrigeration condensing

units. A compressor is a device, which compress a refrigerant gas. When the gas is

later allowed to expand, it absorbs and transfers heat and produces a cooling effect,

which forms the basis for a wide variety of refrigeration products. Their compressors

range in size from fractional horsepower units used in small refrigerators and

dehumidifiers to large units used in commercial air conditioning applications. The

company sells compressors in four major market segments: household ref and

freezers, room air conditioners; commercial and residential unitary central air

conditioning systems and commercial devices including freezers, dehumidifiers,

water coolers and vending machines. The company sells compressors to original

equipment manufactures and cool products distributors.

Tecumseh Products Company (TPC) global visions of providing comfort,

health and convenience to millions worldwide, gives an impetus for the company’s

steady diversification into new frontiers. And today, this cooling giant’s products are

available in over a 100 countries across the globe. TTC entered India through a dual

acquisition of seal compressor limited. Hyderabad and the compressor division of

whirlpool India limited (TPIL) is a fully subsidiary of TPC. TPIL is the largest

independent manufacturer of compressors in the country.

2. Introduction to 7QC tools

Production environments that utilize modern quality control methods are

dependant upon statistical literacy. The tools used therein are called the seven

quality control tools. These include:

1. Check sheet

2. Pareto Chart

3. Flow Chart

4. Cause and Effect Diagram

5. Histogram

6. Scatter Diagram

7. Control Chart

2.1 Check sheet

The function of a check sheet is to present information in an efficient, graphical

format. This may be accomplished with a simple listing of items. However, the utility

of the check sheet may be significantly enhanced, in some instances, by

incorporating a depiction of the system under analysis into the form.

Quality I mprovement: Problem Solving

Check SheetCheck SheetCheck Sheet

ShiftsShifts

Defe

ct T

ype

Defe

ct T

ype

7 Quality Tools7 Quality Tools

Fig no. 2.1: Check sheet

2.2 Pareto Chart

Pareto charts are extremely useful because they can be used to identify those

factors that have the greatest cumulative effect on the system, and thus screen out

the less significant factors in an analysis. Ideally, this allows the user to focus

attention on a few important factors in a process.

They are created by plotting the cumulative frequencies of the relative

frequency data (event count data), in descending order. When this is done, the most

essential factors for the analysis are graphically apparent, and in an orderly format.

For example, A Pareto charts are shown as:


Pareto chartPareto chart


28

16

12 12

64 3

0

5

10

15

20

25

30

LooseThreads

Stitchingflaws

Buttonproblems

Materialflaws

% C

om

pla

ints

Fig 2.2 Pareto chart 1


Pareto Pareto ChartChart

Perc

ent

from

each c

ause

Causes of poor quality

0

10

20

30

40

50

60

70(64)

(13)(10)

(6)(3) (2) (2)

Fig 2.2(a) Pareto chart 2

2.3 Flowchart

Flowcharts are pictorial representations of a process. By breaking the process

down into its constituent steps, flowcharts can be useful in identifying where errors

are likely to be found in the system.

“Draw a flowchart for whatever you do. Until you do, you do not know what

you are doing, you just have a job.”

-- Dr. W. Edwards Deming

The symbols used in flowcharts can be explained as:


FlowchartFlowchart

Activity

DecisionYesYes

NoNo


Fig no. 2.3 Flow chart symbols

A typical Flowchart can be shown as:


FlowchartFlowchart

Fig no. 2.4 Flow chart

2.4 Cause and Effect Diagram

This diagram, also called an Ishikawa diagram (or fish bone diagram), is used to

associate multiple possible causes with a single effect. Thus, given a particular

effect, the diagram is constructed to identify and organize possible causes for it.

The primary branch represents the effect (the quality characteristic that is intended

to be improved and controlled) and is typically labeled on the right side of the

diagram. Each major branch of the diagram corresponds to a major cause (or class

of causes) that directly relates to the effect. Minor branches correspond to more

detailed causal factors. This type of diagram is useful in any analysis, as it illustrates

the relationship between cause and effect in a rational manner.

Fig no. 2.5 Ishikawa diagram

An example of Ishikawa diagram in solving a Quality problem is shown here:


Fishbone Diagram Fishbone Diagram

QualityProblem

MachinesMeasurement Human

ProcessEnvironment Materials

Faulty testing equipment

Incorrect specifications

Improper methods

Poor supervision

Lack of concentration

Inadequate training

Out of adjustment

Tooling problems

Old / worn

Defective from vendor

Not to specifications

Material-handling problems

Deficienciesin product design

Ineffective qualitymanagement

Poor process design

Inaccuratetemperature control

Dust and Dirt

Fig no. 2.6 Fishbone diagram

MANMACHINE

METHOD

QUALITY PROBLEM

MATERIAL

2.5 Histogram

Histograms provide a simple, graphical view of accumulated data, including its

dispersion and central tendency. In addition to the ease with which they can be

constructed, histograms provide the easiest way to evaluate the distribution of data.

For example, Histograms for some data to understand are as depicted:


HistogramHistogram

0

5

10

15

20

25

Category

Fre

qu

en

cy


Fig no. 2.7 Histogram

2.6 Scatter Diagram

Scatter diagrams are graphical tools that attempt to depict the influence that one

variable has on another. A common diagram of this type usually displays points

representing the observed value of one variable corresponding to the value of

another variable.


Scatter DiagramScatter Diagram

.

Fig no. 2.8 Scatter diagram

2.7 Control Chart

The control chart is the fundamental tool of statistical process control, as it indicates

the range of variability that is built into a system (known as common cause

variation). Thus, it helps determine whether or not a process is operating

consistently or if a special cause has occurred to change the process mean or

variance.

The bounds of the control chart are marked by upper and lower control limits that are

calculated by applying statistical formulas to data from the process. Data points that

fall outside these bounds represent variations due to special causes, which can

typically be found and eliminated. On the other hand, improvements in common

cause variation require fundamental changes in the process.


Control ChartControl Chart

18

12

6

3

9

15

21

24

27

2 4 6 8 10 12 14 16

Sample number

Num

ber

of

defe

cts

UCL = 23.35

LCL = 1.99

c = 12.67

Fig no. 2.9 Control chart

2.8 Keys to Successfully Using the Seven Q.C. Tools

Mental Attitudes

Keen awareness to the actual problem.

Eagerness to solve problem.

Be highly motivated for the challenge

Four Specific Keys

Understand the problem

Select the right tool for the job

Obtain appropriate verbal data

Interpret analytical results

Understand the problem

Stage 1 - problem is unclear and not obvious what exact issue should be addressed.

Stage 2 - problem is obvious, but causes unknown explore causes and single out

valid ones.

Stage 3 - problem and causes are known required action is unknown strategies and

plan must be developed.

Selecting Right tool for the Job

Stage 1 - Collect verbal information on events (Brain storming).

Stage 2 - Choose tool to identify causes (Pareto Diagram).

Stage 3 - List strategies and activities (Fishbone Diagram)

Stage 4 - Now plan actual activities (Flow charts).

Obtaining appropriate verbal data

Three types of verbal data:

Facts; factual observations expressed in words.

Opinions; factual information colored by opinion.

Ideas; New concepts created by analyzing facts.

.Group Discussions:

Ensures common understanding.

All data should be without bias or distortion.

Data should fit objective of the analysis

Interpreting Analytical Results

Information must be obtained for accomplishing objectives from:

- Completed diagrams.

- Process of completing diagrams.

Analyze actual information obtained:

- Prepare summarized report with findings, conclusions and processes used.

- Check if necessary data has been obtained, if not Discover the cause and

take appropriate action.

2.9 Summary

The tools listed above are ideally utilized in a particular methodology, which typically

involves either reducing the process variability or identifying specific problems in the

process. However, other methodologies may need to be developed to allow for

sufficient customization to a certain specific process. In any case, the tools should

be utilized to ensure that all attempts at process improvement include:

Discovery

Analysis

Improvement

Monitoring

Implementation

Verification

3. STATISTICAL PROCESS CONTROL

3.1 Definition

Statistical process control (SPC) is a method of visually monitoring

manufacturing processes. With the use of control charts and collecting few but

frequent samples, this method can effectively detect changes in the process that

may affect its quality. Under the assumption that a manufactured product has

variation and this variation is affected by several process parameters, when SPC is

applied to "control" each parameter the final result trend to be a more controlled

product. SPC can be very cost efficient, as it usually requires collection and charting

data already available, while "product control" requires accepting, rejecting,

reworking and scrapping products that already went through the whole process

3.2 General Information on SPC

Classical quality control was achieved by inspecting 100% of the finished

product and accepting or rejecting each item based on how well the item met

specifications. In contrast, statistical process control uses statistical tools to

observe the performance of the production line to predict significant deviations

that may result in rejected products.

The underlying assumption is that there is variability in any production process:

The process produces products whose properties vary slightly from their designed

values, even when the production line is running normally, and these variances

can be analyzed statistically to control the process. For example, a breakfast

cereal packaging line may be designed to fill each cereal box with 500 grams of

product, but some boxes will have slightly more than 500 grams, and some will

have slightly less, in accordance with a distribution of net weights. If the

production process, its inputs, or its environment changes (for example, the

machines doing the manufacture begin to wear) this distribution can change. For

example, as its cams and pulleys wear out, the cereal filling machine may start

putting more cereal into each box than specified. If this change is allowed to

continue unchecked, more and more product will be produced that fall outside the

tolerances of the manufacturer or consumer, resulting in waste. While in this case,

the waste is in the form of "free" product for the consumer, typically waste consists

of rework or scrap.

By observing at the right time what happened in the process that let to a change,

the quality engineer or any member of the team responsible for the production line

can troubleshoot the root cause of the variation that has crept in to the process

and correct the problem.

SPC indicates when an action should be taken in a process, but it also indicates

when NO action should be taken. An example is a person who would like to

maintain a constant body weight and takes weight measurements weekly. A

person who does not understand SPC concepts might start dieting every time his

or her weight increased, or eat more every time his or her weight decreased. This

type of action could be harmful and possibly generate even more variation in body

weight. SPC would account for normal weight variation and better indicate when

the person is in fact gaining or losing weight.

3.3 Introduction TO SPC Techniques

Quality of Design and Quality of Conformance :

“Quality of Design” is the level of quality, a company plans to achieve for its

product.

In general, costs rise as this level is raised.

“Quality of Conformance” is the difference between the actual quality of a product

and its designed quality (i.e., the quality for which the company aims).

The Relationship between Quality, Cost and Productivity

Quality Cost Productivity

Quality of Design ↑ ↑

Quality of

Conformance

↑ ↓ ↑

Manufacturability

of the Design

↑ ↓ ↑

Relationship between Quality, Cost and Productivity

In quality (conformance) control, we set quality levels for groups of products and we

control these levels company-wide.

On the shop floor, we try to control the process in such a way that we will obtain

product lots with specified statistical distributions. (present requirement Cp, Cpk

≥1.33).

Statistical quality is not fixed; it always has a range of variation and is a living entity

that changes according to the technical and economic conditions and advances in

process capabilities.

Earlier Cp, Cpk ≥1 was acceptable standard. Subsequently it was revised to Cp,

Cpk ≥ 1.33. Now this standard is being revised to Cp, Cpk ≥ 2 as a part of six sigma

implementation.

Quality Standards:

To follow up the Quality standards, a random sample of about 60 to 80 units

produced during a period (which may be a day, a week, a month or even a year or

sample from a lot produced) must be considered.

Now draw a histogram for quality characteristics, which may be

dimensions on a part, performance of a product, moisture % purity, finish,

etc.

And now if the histogram looks as normal centered and within 75% of

acceptance limits then only the performance is said to be as per the

“Quality Standards”.

If the histogram has any other shape then the quality is not good enough

for the customer satisfaction.

3.4 Statistical Concepts:

Following are the concepts we need to know:

Average (Symbol X bar)

Average (Symbol X bar)

Range (Symbol R)

Standard Deviation(Symbol σ)

Normal Distribution

S. no. Inspection Standard Quality Standard

01. Too many inspectors (5-10% of

total employees)

Very few inspectors (less than 1% of

total employees)

02. High rejections and rework Very low rejections and rework

03. High Quality = High Cost

(For the same design)

High Quality = Low Cost

(For the same design)

04. Difficult to introduce changes in

process

Process changes can be introduced

quickly

05. QA/ QC responsible for Quality Manufacturing is responsible for quality

06. New process/ product

introduction is slow

New process or product introduction is

quick and economical

07. High customer complaints rate Low customer complaint rate

08. CAPA are slow and

confusing/recurring.

CAPA are quick and clear, effective/

everlasting.

09. Difficult to increase productivity High productivity is possible

10. Worker carries the burden of

Quality and Productivity

Process carries the burden of Quality

and Productivity

Inspection standard Vs Quality standard

Average:

To find average add all the readings noted and divide the total by the number of

readings.

Range:

Range is the difference between the largest and the smallest reading. It shows the

total spread of the readings that are noted during the experiment.

Standard Deviation:

Standard Deviation is a figure calculated from collected data.

It indicates the variation of production process.

It tells us about the dispersion or spread of the data around the average.

If the process variation is limited, then most of the individual readings will

be near the average. In this case value of standard deviation will be small.

If the process variation is large, the data will be more spread or more

dispersed around the average. In this case the value of standard deviation

will be high.

Standard deviation for the sample taken is calculated using the following

formula :

σ = √ {[(Ҳ-x1)2 + (Ҳ-x2)2 +…….. (Ҳ-xn) 2]/(n-1)}

Where Ҳ is Average of readings;

xn is the nth reading;

n is no. of readings.

Normal Distribution:

As we have seen earlier, the data is distributed around the average.

Some readings lie below the average and the others lie above the

average.

If individual readings are distributed around the average on the both

sides symmetrically and most of the readings are near the average and

very few lie away from the average, then the data is said to be

‘normally distributed.

The figure looks like a bell and is called bell shaped curve.

Bell curve

Process Capability (Cp):

An important step in SPC is to establish process capability. A capable process is

capable of maintaining variation with in specification limits required for the job.

Process capability is expressed in terms of an index called process capability index

(Cp).

Cp = T / 6 σ

Where,

T = U.S.L – L.S.L.

σ is process variation.

Ex: Suppose you take 100 readings on the shop floor for a particular

characteristic (which may be length, width, diameter, hardness, moisture

percentage, etc.). Draw a Histogram from these readings. Find ‘X’ and ‘σ’ for these

readings. If the distribution is normal we can talk about process capability.

Machine Capability Index (Cm):

A machine capability index is the one which reveals the capability of the machine to

meet the tolerance and it is expressed in terms of a ratio as follows:

Cm = (Tolerance)/ (6 σ for machine)

Since Process Capability index should be greater than 1.33 it may be preferable to

achieve an index of 1.67 or even 2 for machine capability. However, if improvement

in machine capability involves expensive modifications, check overall process

variation before making such modifications. If variations from the other factors such

as material, method and man are negligible, then machine capability index of 1.5 to

1.66 may be sufficient for the process.

Machine Capability study:

Since Cm <2, machine is not capable of meeting the specifications.

If Cm< 2, machine capability should be improved.

Using cause and effect analysis, causes of variation can be identified.

Once causes are identified they can be removed or controlled to reduce

the variation from machine.

If variations are reduced, machine capability will be improved.

Variation of a machine can be traced to worn out parts or play or run out

or other machine problems.

Repeat capability study after making improvements and ensure machine

capability.

This process may have to be repeated a few times before machine

capability is obtained.

Conclusion:

As described above, we are trying to reduce the rejection rate of

components with the implementation of 7 QC Tools and SPC techniques which

thus will improve the Process capability (Cp) and Machine capability (Cm).

4. MEASUREMENT SYSTEM ANALYSIS

4.1 OBJECTIVE:

The objective of measurement system analysis is to obtain information related to

amount and type of variations associated with measurement system when the

system interacts with environment.

Just as processes that produce a product may vary, the process of obtaining

measurements and data may have variation and produce defects. Measurement

systems analysis (MSA) evaluates the entire process of obtaining measurements

to ensure the integrity of data used for analysis (usually quality analysis) and to

understand the implications of measurement error for decisions made about a

product or process.

MSA analyzes the collection of equipment, operations, procedures, software and

personnel that affects the assignment of a number to a measurement characteristic.

A Measurement Systems Analysis considers the following: selecting the correct

measurement and approach, assessing the measuring device, assessing

procedures & operators, assessing any measurement interactions, and calculating

the measurement uncertainty of individual measurement devices and/or

measurement systems.

Common tools and techniques of Measurement Systems Analysis are: Attribute

Gage Study, Gage R&R, ANOVA Gage R&R, and Destructive Testing Analysis. The

tool selected is usually determined by characteristics of the measurement system

itself.

The Measurement Systems Analysis process is defined in a number of published

documents including the AIAG's MSA (Measurement Systems Analysis) Manual,

which is part of a series of inter-related documents the AIAG controls and publishes.

These manuals include:

The FMEA and Control Plan Manual

The SPC (Statistical process control) Manual

The MSA (Measurement Systems Analysis) Manual

The Production Part Approval Process (PPAP) Manual

The AIAG (Automotive Industry Action Group) is a non-profit association of

automotive companies founded in 1982. What is measurement system

4.2 SCOPE:

It is a must for all measurement tests and equipments referenced in control plans at

all levels and for all types.

4.3 Measurement variation

MSA comprises evaluation of 5 types of variations present in measurement system

in a statistical manner.

Bias

Linearity

Stability

Reproducibility

Repeatability.

Bias:

It is the difference between the observed average of measurement and the

reference value.

Linearity:

It is the difference in the Bias values through the expected operating range of

instrument.

Stability:

It is the total variation in the measurement obtained with the measurement system

on the same part when measuring a single characteristic over an extended time

period.

Reproducibility:

It is the variation in the average of measurements made by different persons using

the same measuring instrument while measuring the same characteristics. This is

also known as Appraiser Variation (AV).

Repeatability:

It is the variation in measurements obtained with one measuring instruments when

used several times by same person while measuring the identical characteristic of

the same part. This is also known as Equipment Variation (EV).

Repeatability and Reproducibility are often major contributors to the

variations. The evaluation of variations contributed by Repeatability and

Reproducibility is called GAGE R&R study.

True Value or Reference value:

A True value is that which is obtained by perfect measurement and these values are

indeterminate by nature. They can only be defined through under the conditions that

uniquely exist when they are considered.

4.4 Preparation for measurement system study:

Inform Appraisers

Avoid obvious mistakes.

No. of Appraisers – 3

No. of samples – 10

Repeat the experiment thrice.

Selected Appraisers must be normally operating the measurement

system.

Sample parts must be selected from the process and represent its

entire operating range.

Least count of 10% tolerance is acceptable.

Procedure to measure.

Measurements to be taken in random order.

4.5 Methods of GAGE R&R study:

Range method

Average and Range method.

ANOVA method.

Popular method of R&R variation analysis is Average and Range method.

Average and Range method:

It is typically done through 3 actual operators and 10 randomly chosen parts.

The properties of a good measuring system are:

Measurement system must be in a state of statistical control.

Variation present must be only due to common causes.

Variability of measurement system must be small compared to

manufacturing process variability.

Variability of measurement system must be small when compared to

specification limits.

4.6 Utility of GAGE R&R:

A criterion to accept new measuring equipment.

For comparing one measuring device with another.

Basis for evaluating a Gage suspected of being deficient.

A required component for calculating process variation and acceptability

level for production process.

Information necessary to develop a Gage Performance Curve (GPC), which

5. Grinding wheel

Grinding wheel

5.1 Introduction:

A GRINDING WHEEL is an expendable wheel that carries an abrasive compound

on its periphery. These wheels are used in grinding machines.

The wheel is generally made from a matrix of coarse particles pressed and bonded

together to form a solid, circular shape, various profiles and cross sections are

available depending on the intended usage for the wheel. They may also be made

from a solid steel or aluminum disc with particles bonded to the surface.

Materials used are generally silicon carbide and diamond with a vitrified bonding

agent. In production grinding, a wide array of materials are used. Wheels with

different abrasives, structure, bond, grade, and grain sizes are available. The

abrasive is the actual cutting material, such as cubic boron nitride, zirconium

aluminum oxide, manufactured diamonds, ceramic aluminum oxide, aluminum oxide,

and others.

7 .ROTARY COMPRESSORS

7.1Introduction:

The rotary compressor is not new. This compressor first took root in the early

1920's. The Norge Company did much of the design work to perfect the Rotary

compressor. Not long after this original design, Frigidaire designed a rotary

compressor called the Meter-Miser. These compressors were very popular and

reliable for use in refrigerator applications.

In the early 1950's, General Electric (GE) designed a rotary for room air

conditioning. This rotary was later applied to residential split systems.

In 1972, Fedders Corporation produced a rotary compressor that Fedders used

exclusively in their products for several years. In 1978, Fedders Compressor

Company (now Rotorex) began to sell their compressor to other air conditioning

manufacturers.

In 1982, Tecumseh began seriously to explore the proprietary design and

manufacture of rotary compressors. By 1983 and 1984, Tecumseh had developed a

family of rotary compressors of unusually high efficiency (l0.8 Btu/watt). Tecumseh

called these compressors the RK series.

In 1993, Tecumseh completed an agreement with GE to purchase existing

equipment to manufacture rotary compressors. Earlier", this equipment had been

part of GE's Columbia, Tennessee, compressor operation. In 1994-1995, Tecumseh

installed the improved family of rotary compressors in its Tupelo, Mississippi, plant.

Tecumseh calls these compressors the RG series.

In 1996, Tecumseh increased the Company's rotary production capacity by building

rotary manufacturing facilities in its Sao Carlos, Brazil, operations.

7.2 About Rotary air compressors:

Rotary air compressors are positive displacement compressors. The most

common rotary air compressor is the single stage helical or spiral lobe oil flooded

screw air compressor. These compressors consist of two rotors within a casing

where the rotors compress the air internally. There are no valves. These units are

basically oil cooled (with air cooled or water cooled oil coolers) where the oil seals

the internal clearances.

Since the cooling takes place right inside the compressor, the working parts

never experience extreme operating temperatures. The rotary compressor,

therefore, is a continuous duty, air-cooled or water cooled compressor package.

A rotary compressor is provided with a rotary valve arranged rotatably in the

housing unit. The rotary valve is partially exposed to an interior space of the housing

unit. The interior space is divided into a plurality of variable working spaces. A return

port is formed in the rotary valve for feeding a cooling medium into an inlet port from

the working space. A quantity of the cooling medium discharged from the working

space through an outlet port is changed in accordance with the rotation of the rotary

valve controlled by the actuator.

Rotary compressor model

7.3Parts of Rotary Compressor:

Cylinder block.

Roller.

Outboard bearing.

Crank shaft.

Main bearing

7.3.1 Cylinder block:

An outlet valve for rotary rolling piston compressor, the piston being assembled

within a cylinder block having an attached end wall provided with gas discharge

passage whose outlet end defines an annular valve seat placed in an oblong recess

area circumscribing the valve seat within which a valve blade is located, the area

between the bottom wall with the circumscribing side wall of the oblong recess

and/or with the peripheral wall of the valve seat being curved to reduce the

turbulence of gas leaving the outlet of the discharge passage.

Material:

Shell molded Gray cast Iron.

Hardness:

Hardness of parts to be 170-241 BHN as measured with 3000kg load and 10mm ball

indenter.

7.3.2 Roller:

Disclosed is a roller for use in a rotary compressor, which roller comprising a

sintered body consisting essentially of 0.5-2.0% by weight of C, 1.0-5.0% by weight

of Cu, 1.2-3.0% by weight of Mo and a balance of Fe and unavoidable impurities. In

the sintered alloy, hard particles of Fe-Mo alloy are dispersed in one of pearlitic and

tempering martensitic matrix, and sintered pores of the sintered body is sealed with

tri-iron tetroxide. Resultant sintered body has high wear resistance and scuffing

resistance capable of being used as an inverter type compressor.

Material:

Heat treatment shell cast gray iron material.

Roller

7.3.3 OUT BOARD BEARING:

Plate no. 7.3 out board bearing

Material:

Class G3000 Cast Iron.

Hardness:

Hardness of the parts is to be 170-241 BHN as measured with 3000kg load and a

10mm ball indenter.

7.3.4 CRANK SHAFT:

The crankshaft is made of either forged alloy steel or out of spheroidal gray iron

casting. The portions of shaft that work inside the bearings are called ‘journals’.

The Crankshaft should be strong enough to take the thrust of the piston

during compression, without getting distorted.

The reciprocating motion of the piston is made possible by the ‘crank’ of the

crankshaft. The piston-end of the connecting rod is attached to the piston by the

piston pin, which is tightly fitted in the position.

Crank shaft

7.3.5Main bearing:

Material:

Class G3000 Cast Iron.

Hardness:

Hardness of the parts is to be 170-241 BHN as measured with 3000kg load and a

10mm ball indenter.

Implementation of SPC techniques:

OUT BORED BEARING:

Fits (clearances):

The design of the clearances between parts causes an oil film to fill in the

clearance to reduce leakage (blow by). Excessive leakage not only reduces

compressor performance but also raises the discharge temperature. High discharge

temperatures put more load on the motor, causing it to work harder, and can lead to

premature compressor failure

Exhibit 1 shows a cylinder, roller, and vane assembly to illustrate how the oil film

seals the ends of the roller and vane. The oil is actually reducing leakage between

these parts and the main bearing/outboard bearing faces. This exhibit does not show

the Main bearing/outboard hearing faces.

Also, this exhibit does not show two other leakage areas that are equally

important. These areas are the “suction seal” (o-ring) and the “valve seat”. Leakage

in these two areas has the same effect

Importance of face flatness of a out board bearing

Understanding the geometric tolerances of the parts is the secret of building a

rotary compressor. Part geometry is more important than the fits and finishes.

Each part has many geometric tolerances. Roundness, square ness,

parallelism, taper, flatness, concentricity, etc. Unfortunately, we do not have the time

to review these on every part. The following review should give you an overall

understanding of the importance of geometric tolerances.

Remember, we cannot see these small dimensions with the naked eye. The

following purposely exaggerated exhibits show conditions that are real.

Bearing Roller Face Flatness:

Figures located on the following page show how main and outboard bearing

roller face flatness affects leakage and tight pumps. The flatness of the bearing face

can either be "concave" or "convex.”.

Fig. shows the main bearing face to be concave. In this condition, the face of

the bearing dips away from the roller. This action causes an increase of the

clearance over the roller. Remember the section on roller to cylinder clearance? If

the main bearing face is .0002 out-of-flats in the concave direction and the selected

clearance was .0004, then the total clearance is .0006. If the out board bearing (not

shown) is also .0002 out-of-flats, we have a combined total of .0008 clearances or a

6 percent reduction in performance.

Concave bearing face

Fig. below shows the face of the main bearing face to be convex. In this

sketch, we can see that the bearing face is the opposite of the previous sketch. The

face dips in towards the roller. If we had selected .0004 for the roller to cylinder

clearance, we would not have a problem. However, if the out board bearing was also

.0002 out-of-flats in the same direction there would be no room for oil to lubricate the

parts. This metal to metal contact can damage the parts. Depending on the vane

clearance, the vane can seize during normal operation. In either case, the result is

failure.

Convex bearing face

Hence Flatness is considered as the most important parameter in

manufacturing the Rotary compressor. Since there are a lot of rejections of

compressors in the company because of the variations in Face flatness of out

board bearing, we concentrated to improve the Flatness and bring it to the desired

value. For this, we implemented the Statistical process control techniques and 7

Q.C tools. In further, we discuss the implementation of the above methods and the

results obtained.

In the implementation of SPC techniques for any component, first we have to

study the machine.

OUT BORED BEARING:

In case of OBB, the machine capability calculated is 2.66, which is more than

the required value. So the machine is capable for producing the components within

the required range.

In case, if machine capability is < 2, then using cause and effect analysis,

causes for variation are identified. Once causes are identified, they can be removed

or controlled to reduce the variation from machine. If variation is reduced, the

machine capability will improve.

spc project report

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