ProjectReport_SPCinAM

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Prepared By:
Syed Qamar Raza
SN: 992691057
For Professor : Beno Benhabib
Department of Mechanical and Industrial Engineering,
University of Toronto
Course Project
Statistical Process Control
In Automotive Manufacturing
MIE 1718HF : Computer Integrated Manufacturing
Date: December 01, 2006

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SCOPE
A: What is SPC (Slides 3 to 6)
 Definition
 SPC Tools
B: Why Use SPC (Slides 7 to 9)
 History of Quality / Manufacturing
 Purpose
C: Application of SPC in AM (Slides 10 to
30)
 Basic Application
 Advanced Application
 Failures when SPC was ignored
D: Recent Developments & Research Areas (Slides 31 to
39)
 SPC for FMS (small batches)
 SPC in CIM Environment
 SPC with Six Sigma
 What do we need after SPC

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What is SPC
Definition
A method to monitor, control and improve processes
through the use of statistical techniques by identifying the
type and causes of variation.
Practitioners may then seek ways to remove that variation
from the process.
Brief Description of SPC Tools
The most successful SPC tool is the control chart, originally
developed by Walter Shewhart in the early 1920s.
A control chart is a graphic representation of a particular
characteristic of a process which distinguishes between
common cause variation and special cause variation.
* Tague, N. R., The Quality Toolbox, Second Edition, ASQ Press, 2004

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What is SPC
Different types of control charts can be used, depending upon
the type of data (variable or attribute).
Variables charts
X & R, X & S, MA-MR, CUSUM, EWMA, multivariate chart.
Control charts for variable data are used in pairs.
Top chart monitors the average, bottom chart monitors range.
Attributes charts
 p chart, np chart, c chart, u chart.
 Control charts for attribute data are used singly.

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What is SPC
X Chart R Chart S Chart
UCL: µ + 3σ / √n X-bar+A2R-bar X-bar+A3S-bar
CL: µ R-bar S-bar
LCL: µ - 3σ / √n X-bar-A2R-bar X-bar-A3S-bar
A2 and A3 from standard tables
µ +/- σ = 68.27%
µ +/- 2σ = 95.45%
µ +/- 3σ = 99.73%

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What is SPC
Others also include Sampling and Process Capability
analysis in and as SPC tools and usually employ these tools
in conjunction with control charts.
 Sampling is the selection of a set of elements from a population or
product lot and draw conclusions about the population.
Determinations of sample sizes are readily obtained through the
selection and application of the appropriate mathematical equation or
sampling schemes published as standards e.g. ANSI/ISO Z1.4 or
Z1.9
 Process-capability study is used to assess the ability of a process to
meet specifications. Capability estimates in widespread use include:
• “Cp” and “Cpk” for continuous data.
• “Sigma” for attribute data (i.e., for defect rates).
 Capability estimates like these essentially reflect the nonconformance
rate of a process by expressing this performance in the form of a
single number. Typically this involves calculating some ratio of the
specification limits to process spread.

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Why Use SPC
History of Quality / Manufacturing
1200 – 1799: Age of Craftsmen and Guilds
 Worker autonomy, craftsmen controlled production of products.
1800 – 1899: Product Orientation and Mass Production
 Advent of industrial revolution, mass production and factory system.
 reduced worker autonomy, management controlled production.
 Quality of final product viewed in terms of final inspection to
separate good items from bad and was the responsibility of a separate
inspection department rather than those who producing the product).
1900 – 1945: Process Orientation
 Began after WWI, use of SPC became a cornerstone for industrial
standards, which were applied with widespread success in American
industry during WWII.
 Following the war, industrial standards were largely declassified in
America & manufacturers returned to focus on getting product out.

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Why Use SPC
History of Quality / Manufacturing
After 1945: Birth of Total Quality / Quality Standards
Japan’s struggle and success to enter the int. marketplace.
As quality management gained attention as a critical business
strategy in Industrial countries, voluntary and regulatory
quality standards evolved in many disciplines (e.g.
engineering, communications, medicine etc.)
ISO established worldwide generic standards for a variety of
disciplines. ISO 9000 series of standards is for engineering,
design, and manufacturing industry sector and was first
published in 1987.

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Why Use SPC
Purpose
Increasingly, the development of and rigorous adherence to
standards has become not only a cornerstone of quality but
also a prerequisite for doing business on an international
level.
The objective to use SPC is to move away from traditional
quality control approaches to quality engineering to assure
product quality i.e.
defect prevention instead of defect detection.
Beginning early 1980’s, U.S. manufacturers (GM, Ford and
DCX) started to use and require their suppliers to use SPC
and publish manuals to help, understand and support their
suppliers.*
* Statistical Process Control, Reference Manual, AIAG, issued 1992,
Second Edition issued July 2005

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SPC in AUTOMOTIVE MANUFACTURING
Basic Application
Case Study 1
A U.S. manufacturer, Norplex/Oak, implemented SPC in
1984 as per Ford Motor Company’s requirement.*
The concern was the Quality of a series of thrust washers for
Taurus/Sable automobiles.
Significantly reduced 100% inspctn., improved productivity.
Determined that allowable variation can go up to 22.5%.
Gained understanding of process and equipment capabilities,
accurate history of process performance.
Ford assured end product consistency and quality,
confidence in the supplier’s process.
* Chaudhry, S.S. and Higbie, J.R., Quality Improvement through Statistical Process
Control, Quality Engineering 2(4), pp. 411-490, 1990

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Basic Application
Case Study 2: SPC with Automated Inspection
Roctel Manufacturing Ltd., Guelph, Ontario, Canada, is a
manufacturer of valve housings for rack-and-pinion steering
mechanisms and other automotive steering assembly
housings.
The company found it difficult to track the quality of its
products under the manual SPC that was in place, 1,500 to
2,000 data points to be collected and recorded per day.*
Roctel installed 31 fixture type data collection gauges
connected to an intelligent network interface system, which
was connected to company’s LAN.
Roctel reaped numerous benefits from automated system.
 Customer complaints have decreased by 55% to 10 in 1994.
 33% increase in sales.
 Recouped 300 hours of machine time per month.
* Litsikas, Mary, Roctel’s machine operators seek more knowledge,
Quality 34(5) pp. 40-44, May 1995

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Advanced Application
Case Study 3: SPC with Multivariate Analysis
The push to improve quality has been well illustrated in all
facets of automotive manufacturing.
As technology advances, capability to collect measurement
data over the entire surface of the product has been realized.
A number of automotive plants have already reached 100%
dimensional process control.
This results in a tremendous amount of collected data.
As the number of design points increases, the large quantity
of control charts produce becomes difficult to monitor.

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The analysis of such data presents a challenging problem.
Multivariate control charts exists, but with a large number of
related design points, their interpretation becomes difficult.
In this case study, SPC technique is applied to the results of
multivariate analysis. In this manner, just a few control
charts are required to monitor the design points of the
manufactured product.
The case study is of a:
production process of an automotive steering wheel
at a large Midwest manufacturing plant.
Wheel is produced using two-piece cast iron injection molds.
* Young, R.R., Bauer, K.W. and Shedden J.S., Multivariate Analysis & Statistical Process
Control for Steering Wheel Manufacturing, Quality Engineering, 10(3), pp. 481-489, 1998

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26 design points on the front side of the wheel, located
around the inside of the four spokes which surround the air
bag have been identified for the deviation study.
The data set included 80 observations (80 wheels from 35
molds, a sample of 5 to 7 wheels per mold).
 An observation is a vector of the 26 deviations at each design point.
Multivariate analysis was performed first to reduce the
dimensionality of the data set.
Factor Analysis technique was employed which attempts to
simplify the complex and diverse relationships that exists
among the observed variables by uncovering common
dimensions that link the variables.

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The steps to perform a factor analysis involve:
 Analyzing the correlation coefficients across the variables.
 Determining the number of factors to retain.
 Developing the factor loadings of the variables, and
 Analyzing the results using the factor scores.
The correlation matrix of the data showed a strong
correlation within the spokes of the wheels & between the
diagonal design point groupings & hence, indicated that the
dimensionality of the data set could be significantly reduced.
Factor analysis indicated that 3 common factors stand out
from the rest and explain 91.8% of the total variation.

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Factor loadings determined the highest loading of each
design point and indicated that common factors are strongly
influenced by the spoke design point groupings.
The strong breakout of the spoke design point groupings
indicated that systematic deviations are occurring which can
be easily identified by the factor indices.
Factor scores were calculated from the factor loadings,
which identified the significant deviation pattern (caused by
imperfections in the steel insert of the steering wheel).
 Each observation has three factor scores.
 Observations receiving large factor scores are experiencing
significant deviation.

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Standard SPC technique was applied to factor score indices,
which, by construction have mean of zero and a unit standard
deviation.
X-bar & R charts were than developed, one each, for the
factor score indices that are the results of multivariate
analysis.
Overall, with just 2 charts, the manufacturer monitored the
quality control of 26 design points across 180 wheels from
35 molds.

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Case Study 4: SPC with DOE
Examples of the integration of SPC with DOE are not as
common. One exception would be the recent paper of Cherfi
et al. (2002) which uses SPC to provide direction for
designing an appropriate experiment for:
improving color control in the automotive industry.
Car manufacturers are concerned with ensuring the
homogeneity in colors of the different parts that compose the
exterior of the vehicle (body, bumper, hub cap, etc.).
* Cherfi, Zohra, Bechard, B-M and Boudaoud, Nassim, Color Control in the Automotive
Industry, Quality Engineering 15(1), pp. 161-170, 2002-2003

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The actual tendency is not for opaque colors, but rather for
paints with metallic or pearly effects. The technical
difficulties are therefore accentuated for the manufacturers
since the presence of metallic chips in the paint causes a
variation in the color according to the angle of observation.
Also, the development of metametric colors increases these
difficulties even more, as these colors are sensitive to
different illumination sources.
The spectrocolorimeter for measuring color is indispensable
to process improvement. The color space is the system used
for coding colors and the one used for the present research

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has an almost spherical shape identified as L*a’*b’; where L,
a’, and b’ correspond to the three axes of the space (Fig. 1).
Axis L represents the brightness of
the color. Axes a’ and b’ are the
chromatic axes, a’ being the green–red
axis and b’, the blue–yellow axis.
The spectrocolorimeter is made of 3
sets of flash lamps. The light reflected
by the surface of the specimen to be
measured is collected by the sensors
and analyzed, according to three angles:
25º, 45º, and 75º.
Figure 1

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The 25º angle corresponds to a viewing angle perpendicular
to the specimen.
The colorimetric values of the manufacturers’ samples are
stored in the spectrocolorimeter. Therefore, when measuring,
the difference between the color of the specimen and the
sample is obtained for the three angles considered. Thus, the
color is represented by nine colorimetric coordinates in
reference to the sample.
Nevertheless, the human eye remains the only judge in case
of disagreement between the visual impression and the
results given by the spectrocolorimeter.

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In agreement with the manufacturer, four colors considered
critical have been selected for study in priority (grey, white,
blue and green), using the following two criteria:
 The production volume .
 The amount of demerit points obtained.
A first set of measurements of these four colors has revealed
an important dispersion for the gray and the white colors,
and stable results for the green and the blue colors.
For gray being the most critical color, these results reveal
important dispersions for dL 25º, 45º, and 75º. Strong
disparities are even found within a series.

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This representation emphasizes a bimodal distribution:
a subpopulation of bumpers respects customer specifications
(+/-1.6 compared to target), while the other is offset.
The differences observed about L correspond to the flip-flop
phenomenon, which characterizes metallic colors with low
amount of colored pigments. It causes large differences in
brightness according to the three angles.
Flip-Flop PhenomenonBimodal Distribution

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The cross-examination of the colorimetric results with the
incidents occurring in the process, as well as a prior study
of the four colors reveal the following four factors as
being influential:
 Dry thickness: thickness of the paint after being dried by heating.
 Orientation of the chips (for metallic paints only) which influences
luminosity.
 Stirring of the paint before it is pumped:
 The formulation of the paint itself.
 The first two factors depend on several parameters:
Therefore, for grey and white colors, DOE is used to:
 reveal and quantify the effect of the factors influencing variability.
 determine optimum levels for these factors.

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The factors considered are:
 A: paint flow used for the first base coat (in cc), with three levels.
 B: paint flow used for the second base coat (in cc), with three levels.
 C: proportion of thinner, with three levels.
 D: type of thinner, with two levels.
Two excellent combinations identified as A2B1C2D2 and
A2B1C2D1 & D1 was chosen because of the low cost.
A correlation study allowed further process improvement.
The results achieved are:
 Better understanding of the factors influencing the colors.
 Establishment of process monitoring by means of a
spectrocolorimeter integrated in the production chain.
 The quality index used has been improved by 67% in only 6 months.

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Failed Experiments
Case Study 5: When SPC was Ignored
Another exception would be the research report by Abraham
and Brajac (1995) which illustrates the mistakes which
might happen when these two areas, SPC and DOE,
are not integrated.
In this paper Abraham (University of Waterloo) and Brajac
(General Motors of Canada) categorize all published case
studies into two main groups.
 Application specific (one specific for injection moulding is not
suitable for engineers working in a a foundry).
 Approach specific (used to solve a problem rather than the nature of
the problem e.g. Taguchi methods or response surface methodology).

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Failed Experiments
In this paper, Abraham and Brajac explored and presented
third type of category that is seldom published and rarely
discussed, experiments which were not successful for a
variety of reasons but in which, valuable lessons are learned.
the authors emphasized that a structured approach to be
applied to the process of experimental design.
5 case studies were discussed in the paper, one of which is
The Case of a Crazing Tail Light.
The tail-light consists of two portions:
 The clear back-up lens.
 The surrounding red lens.
* Abraham, B. and Brajac, M., Real Experiments, Real Mistakes, Real Learning, I.I.Q.P.
research report, University of Waterloo, RR-95-05, March 1995

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Failed Experiments
The back-up lens is moulded first and then inserted into another
mould where the red lens polycarbonate material is injected
around the clear back-up lens.
3 other moulds produce one complete assembly per cycle.
The problem was the crazing of the back-up clear lens (fine
cracks that can effect the translucency), which was not noticeable
until the part leaves the oven.
The typical discrepancy rate was 2-3% that was easily
manageable. But for no apparent reason, increased dramatically
to 20-30%.
An extensive cause & effect diagram was constructed including a
number of response variables to ensure that

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Failed Experiments
crazing is not eliminated at the cost of having another
problem.
Two experiments were conducted:
 First focused on injection moulding machine’s parameters. This 32-
run experiment was a failure and very little was learned that could
reduce the scrap rate.
 Second focused on factors involving oven and moulds and clearly
demonstrated that most of the scrap produced came from one mould.
A search for a special cause was made and it was discovered
that during routine preventive maintenance, some minor
changes were made to one of the moulds which were the
source of the problem.

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Failed Experiments
What Was Learned?
The mould and the point where the crazing was first
observable were separated in time & space. Thus cause and
effect relationship was not readily observable.
It would have been an easy matter to stratify the scrap my
mould since each tail light carries a mark that indicates
which mould produced it.
A histogram of the scrap and/or control chart would easily
have isolated the source and special cause of the problem.
The eagerness of the team to put into practice the recently
learned DOE ignored the power of seven basic quality tools
(Ishikawa, 1976).

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Recent Developments and Research Areas
SPC for Flexible Manufacturing Systems (FMS)
The manufacturing environment has undergone many
significant changes since Shewhart first developed SPC
charts in 1920’s.
The drive towards flexible automation poses a major
challenge to quality practitioners by threatening to invalidate
the traditional SPC techniques.
 SPC techniques are based on the study of a sample that can represent
a population. When the production batch is one or small, any form of
sampling becomes synonymous with 100% inspection.
 For the same reason, it is not possible to perform process capability
studies.
With the introduction of flexible manufacturing systems
designed for the production of very small batches, there is a
need to have a method to apply SPC techniques to small
batch or one-off production.

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SPC for Flexible Manufacturing Systems (FMS)
In this paper, Cullen* presents delta x model. The key idea is
that it is possible to construct a population of similar, but not
necessarily identical features on a variety of parts, and to use
this population to study the manufacturing process.
The requirement of a data processing system to support the
delta x approach must include facilities to:
 Identify a class of features over a range of different part numbers.
 Standardize the difference between target and actual dimensions.
 Calculate the values of mean and st. deviation for a pre-set sample.
 Identify significant shifts in either mean or standard deviation.
Unless this type of approach is followed, automated
inspection simply identifies faulty products when it is too
late to do anything about them.
* Cullen, J.M., The Use of Statistical Process Control in One-Off and Small Batch Production,
Proc. 8th Int. Conf. Automated Inspection & Product Control, pp. 63-68, June 1987

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SPC in CIM Environment
with the ongoing trend towards the automation of industry,
there are foreseeable changes in SPC design and
implementation. It is essential for SPC techniques to be
integrated into computer-aided manufacturing processes.
With the help of automation, retrieval of online data is be
coming cheap and convenient, and 100% inspection of every
manufactured part is not difficult to achieve.
Along with the increasing amount of available data, data
properties are also changing, and this may undermine some
basic assumptions regarding the implementation of SPC.
Some traditional SPC ideas and criterion will fail to adapt to
the new environment, while others will continue to function
and gain additional importance.
* Cai, D.Q., Xie, M. and Goh, T.N., SPC in an Automated Manufacturing Environment,
Int. J. Computer Integrated Manufacturing, Vol. 14, No. 2, pp. 206-211, 2001

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SPC in CIM Environment
Problems with traditional SPC
The assumption of independently distributed data fails to hold
in a CIM environment. The nature of online data is auto-
correlated.
Non-normality is another common problem. Sample sizes in
automated processes are quite small.
General solutions for Data Non-Normality
Convert the particular non-normal distribution into a normal
distribution, followed again by the traditional chartings OR
Plot the original data onto special charts, such as the EWMA.
Both methods are meant to counteract the influence of
violations of the assumptions.

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SPC in CIM Environment: Integration with EPC
Apart from the possible changes that may happen to SPC
itself, automation also offers SPC the opportunity to combine
with other process regulating methods such as engineering
process control (EPC).
One of the bridges between SPC and EPC is the common aim
of reducing process variation.
 In the use of SPC, control charts are applied to discover and
distinguish common causes and special causes, which are used to
remove variations.
 In the use of EPC, feedback/ feedforward control is employed to
compensate for variations.
Hence, SPC can improve system performance but cannot
maintain it, especially when the process is subject to wear and
tear. In contrast, EPC can maintain the system performance at
a specified level but cannot improve it (Figure 1).

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Original Process ( µ, σ )
Figure 1. Comparison of process improvement under pure SPC and pure EPC.
(a) Process implemented with pure SPC ( µ1, σ1 )
(b) Process implemented with pure EPC ( µ2, σ2 )
(a) µ1 improved but drifting, σ1 decreased but deteriorating
(b) µ2 not improved but steady, σ1 decreased and steady

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In SPC, noise is termed a ‘common cause’ , and system
irregularities are termed an ‘assignable cause’ , for which
corrective action is possible. Usually data correlation is
considered an assignable cause, although it is not easily
removable. In such a situation, the feedback controls of EPC
rather than traditional SPC corrective actions should be
considered to compensate the process change. In this way,
EPC becomes a part of SPC in general practice (Figure 2).
Process Monitoring
SPC
Corrective Action
Error CompensationCause Removal
EPC
Figure 2. Functional combination of SPC and EPC.

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SPC with Six Sigma Methodology
Since its development in late 1980’s by Motorolla, most of the
automotive manufacturers (also the aerospace, electronics and
service industry) have been moved to Six Sigma methodology
Six Sigma is a set of tools / problem solving methodology.
 In Six Sigma’s D-M-A-I-C methodology, problems are considered as a full
project and “define, measure, analyze, improve and control” approach is
followed.
 SPC is used in the control phase of D-M-A-I-C methodology.
In traditional SPC with 3 sigma limits, the fallout of the process in
0.027% (99.73% values lie within 3-sigma limits). For high
production volume, this fallout is considerable. With 6 sigma
limits, the maximum allowable fallout is 3.4ppm.

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Quality in the 21st
Century
What do we need after SPC?
In most of the quality related issues, the “specifications” are
seen as the perfect and complete projection of customer
requirements into technical performance requirements, but;
 Owing to the influx of new (often software/digital electronics based)
technology, fundamental nature of many products is rapidly changing.
 It is no longer possible to completely define technical performance
requirements in terms of product specifications (validity).
 Even if the specifications were complete, it would be a huge challenge to test
whether a given product would meet specifications (Verifiability).
The author lists a few research papers that look into the nature
of the specification superficially in the context of modern
product creation and invites authors to address this issue in
their future research.
* Brombacher, A.C., Quality Control in the 21st Century: What do we need after SPC?
Qual. Reliab. Engng. Int. 2006; 22:731–732, Published online in Wiley InterScience
(www.interscience.wiley.com). DOI: 10.1002/qre.844

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