36. Quality Insurance, Testing, and Inspection

Product Quality Quality Assurance Total Quality management Taguchi methods The ISO and QS Standards Statistical Methods of Quality Control; Reliability NDT Automated Inspection

Quality

Continuous Improvement in quality– Never-ending improvement (kaizen in

Japan)– Quality must built into a product

Quality; customer satisfaction customer amazement

Dr. Deming(1900-1993) in Japan, 1954 Total Quality Management, TQC

TQM Defect prevention rather than detection

– It is too late to detect at the end of process– 100 % inspection– Only a few cents part can ruin an expensive

product– Customer satisfaction if not lost money

Leadership, team work– Only managers can make things worse– Eliminate fear, eliminate slogan, quota

Continuous Improvement

Dr. Demming Management must commit to quality High quality doesn’t increase cost. Bad

quality actually increase costs Break down barriers to workers

(eliminate fear) Don’t blame system failures to workers. Recognize and Increase workers

potential

Dr. Demming Recognize pride of workmanship. Avoid

slogans (zero defect), posters, numerical goals (always increases), and production quota

Statistical process control, vendor provides SPC, JIT

Teach statistics to workers to improve quality

Institute training system

Taguchi Methods

Dr. Demming’s disciple Poor quality customer dissatisfaction Costs incurred to service and repair

defective parts Credibility diminishes in the market

place The manufacturer will lose market share

ISO 9000 standard 1987 (1994 revision), ISO 9000 standard

(Quality Management and quality Assurance Standard) Statistical Process Control– ISO 9001 Model for quality assurance in

design/development, production, installation, and servicing

– ISO 9002 Model for quality assurance in production and installation

– ISO 9003 Model for quality assurance in final inspection and test

– ISO 9004 Quality management and quality system elements-Guidelines

Why Statistics? Cutting tools, dies, and molds are subject to

wear dimensions vary Machinery perform differently on its age,

condition and maintenance Metalworking fluid degrades surface finish, tool

life, and forces are affected Environment (Temperature, humidity, air quality)

may change Different shipment of raw material Operator skill and attention varies Chance variation (random) Assignable variation (with specific cause)

Statistical Quality Control Sample size; the number of parts to be

inspected Random sampling Population (universe) Lot size The method of variables; quantitative

measurements of dimension, tolerances, surface finish, physical & mechanical properties

The method of attributes; Qualitative

Statistical Quality Control Distribution

– Frequency distribution –e.g. bar charts– Normal distribution curve (Gaussian)

Arithmetic mean Dispersion

– Range R = xmax-xmin

– Standard deviation =sqrt {(xi-x0)2/(n-1)}

Manufacturing processes can be judged to be in control by using statistical measures.

The quality of a product can be measured by observing attribute values or variable values.

Attributes are discrete measures such as number of cracks on a surface or number of defective resistors.

Variables are continuous measures of a characteristic such as length, weight, hardness, etc.

The statistical quality control techniques differ for attribute and variable measures.

The discussion that follows concerns statistical quality control based on variables

Two basic questions that a statistical quality control program can answer are:

1. Has the average value of a product characteristic remained within acceptable bounds?

2. Has the variability of a product characteristic remained within acceptable bounds?

Being able to answer yes to one of these questions does not necessarily affirm the other.

To answer the first, an chart_can be used and for the second question, a R chart. Both of these charts utilize the confidence interval concept that has been presented earlier.

Both use a sequence of samples that are taken over a period of time in order to provide the evidence needed to answer these questions.

x

Statisticians have also developed methods of establishing these confidence intervals that use simple calculations based on prepared tables.

What follows is a presentation of the methods without providing the statistical arguments to justify their use.

Another concept that is common to both of these charting techniques is that one first has to establish the confidence intervals that represent the process when it is operating satisfactory (in control).

Some degree of good judgment, process knowledge, and historical information is needed in developing these "in control" criteria.

The methods that are presented below are based on the premise that the process is in control and that samples from the process can be used to establish these "in control" confidence intervals.

To proceed on this basis, the sample size has to be pre-established and continuously used during later process monitoring.

SPC If a machine is not in good condition, manager

can’t blame workers for bad products find reason and fix it from SPC

Control charts– Sample size from 2-10 (sample size held constant

throughout the inspection)

– Frequency of sampling; case by case

Control limits; average value– UCL=x0+3x0+A2Ř where Ř is the average of R

– LCL=x0 - 3 = x0 -A2Ř

Let n be the size of each sample. Let m be the number of samples that are collected during the "in control" period of time. For each sample, compute the mean and range, (maximum value - minimum value).

The mean is going to be used to evaluate average performance and the range will be used to evaluate process variability.

The range can be statistically correlated to the standard deviation, and is much easier and faster to compute

SPC Control limits; average value

– UCL=D4 Ř – LCL= D3 Ř

Ř/d2

In good statistical control; inside the boundary Real-time SPC; computer system with

electronic measurements Process capability; limits within which

individual measurement values resulting from a particular manufacturing process normally be expected to fall when only random variation is present.

Constant for Control Charts

S.S A2 D4 D3 d2

2 1.880 3.267 0 1.128

3 1.023 2.575 0 1.693

4 0.729 2.282 0 2.059

5 0.577 2.115 0 2.326

6 0.483 2.004 0 2.534

S.S. Sample Size

Example Measuring the length of machined

workpieces. Sample size 5, sample number 10, so total 50 parts

X0=44.296/10=4.430 in Ř=1.03/10=0.103 in A2=0.577, D4=2.115, D3=0 (from sample size

5)– UCL=x0+A2Ř=4.430+0.577*0.103=4.489– LCL= x0 -A2Ř =4.430-0.577*0.103=4.371– also– UCL=D4 Ř =2.115*0.103=0.218 in– LCL= D3 Ř =0*0.103=0 in

Ř/d2 =0.103/2.326=0.044 in

Sample sizex1 x2 x3 x4 x5 xave R1 4.46 4.40 4.44 4.46 4.43 4.438 0.062 4.45 4.43 4.47 4.39 4.40 4.428 0.083 4.38 4.48 4.42 4.42 4.35 4.410 0.134 4.42 4.44 4.53 4.49 4.35 4.446 0.185 4.42 4.45 4.43 4.44 4.41 4.430 0.046 4.44 4.45 4.44 4.39 4.40 4.424 0.067 4.39 4.41 4.42 4.46 4.47 4.430 0.088 4.45 4.41 4.43 4.41 4.50 4.440 0.099 4.44 4.46 4.30 4.38 4.49 4.414 0.19

10 4.42 4.43 4.37 4.5 4.49 4.436 0.12Average of average4.430 0.103

Acceptance Sampling and Control

1920s, WW II, MIL STD 105 If a certain % is exceeded, the whole lot is

rejected Probability; relative occurrence of an event Acceptance Quality level (AQL)

– 95% probability of acceptance– Consumer knows that 95% acceptable

(consumer’s risk)– Producer’s risk; good parts are rejected (5%)

Rejected lots are salvaged; greater cost

Reliability, Testing and Inspection

Reliability; the probability that a product will perform its intended function in a given environment and for a specified period of time without failure.– Series reliability– Parallel reliability; back-up system, redundant system

Non-destructive testing (NDT)– Liquid penetrants technique– Magnetic-particle inspection; apply fine ferromagnetic

particles (sometimes dyed) on the surface, then magnetized. Flaws can be seen

– Ultrasonic Inspection; put into couplant(water,oil, glycerin, grease), 1-25 MHz

– Acoustic methods; pick up by piezoelectric ceramics– Acoustic Impact technique

Reliability, Testing and Inspection

Radiography; X-ray– Digital radiography– Computed tomogrphy

Eddy-current Inspection; using electromagnetic induction

Thermal inspection; heat sensitive paints, papers, liquid crystal

Holography– Holographic interferometry– Acoustic holography

End of Ch 36 Quality