DOE-Taguchi Knowledge Base
DOE-Taguchi Knowledge Base
DOE-Taguchi Knowledge Base
#qt4-030 Troubleshooting
#qt4-040 How to get Support
#qt4-050 How to Give Feedback
#qt4-060 Release Notes
#qt4-070 Upgrade Information
#doe-210 Topic Overviews (20 topics)
#doe-220 Subject Index (How To . . )
#doe-230 About the Taguchi Methods
#doe-240 About Nutek, Inc.
About Qualitek-4
Qualitek-4 (QT4) Version 6.6 is a Windows software for Automatic Design and Analysis of
Taguchi Experiments. The software was originally introduced in 1987 as Qualitek-3, and later
enhnced and released in 1991 as Qualitek-4 for DOS and in 1996 as Qualitek-4 for Windows (3.1).
The version for Microsoft Windows is based on the DOS version, but, represents completely
redisigned screens, user interfaces, and several technical capabilities and is compatible with
Windows 95, 98, ME, 2000, and XP.
The program comes with over 50 example experiment. These example experiments include all
exercises in the text: A Primer on The Taguchi Method, all problems and exercises in the Nutek
seminar handout, and solutions to many other example exaperimental projects.
Program Capabilities:
L-4, L-8, L-9, L-12, L-16, L-18 ... L-32 and L-64 arrays, 2, 3 and 4
level factors, 2 to 63 factors, Std. and S/N analysis using BIGGER, SMALLER
or NOMINAL, and dynamic quality characteristics.
Program Size: About 12 MEG. You will need an IBM/Compatible 486 or better computer.
APPENDIX A
If you are a current user of Qualitek-4 (DOS), you may upgrade to the Windows version any time
before December 20, 1997 for only $665 (USD). If you purchased our DOS version after April15,
1996, you can upgrade to the Windows version for a special price of only $298.75 ( 25% of the list
price of $1,195). This special upgrade price is valid till Jun 1, 1997.
Shipping and Handling: $ 20 within the USA, $ 30 Canada, $45 - $85 all other countries.
We ahve two other software, Qualitek-1 and Qualitek-2. Subject and content wise these two
packages bear no relations to Qualitek-4 or the Taguchi technique.
Qualitek-1 is for Component Durability Test Planning. It does all the statistical calculations
necessary to determine the statistically valid test parameters. It is generally used by the professionals
involved in the durability or reliability test setups.
Qualitek-2 is for Weibull Analysis. Weibull analysis is statistical technique to determine reliability
status of failure test data and predict the behavior of the population performance. It is a popular
technique used by the test and validation activities of major manufacturing organizations.
Subject: Qualitek-4 Demo Software Evaluation & Quick Guide (Print for reference)
Thank you for downloading the working version of our Qualitek-4 software from our web site
(http://www.rkroy.com ). If you are about to use our software for the first time, we strongly recommend
that you print this mail and keep it for your reference.
The software you downloaded contains over 50 example experiments of all sizes, which you can
review to explore most capabilities of the software. Should you need to unlock the full range of its
capabilities for unrestricted immediate use, obtain your personal Registration number by placing an
order with us. If you are a student, continue to use the limited capability of Demo without any
obligation. To purchase Qualitek-4, please visit www.rkroy.com/wp-q4w.html and follow the purchase
instruction.
Sincerely,
Paula
Customer & Sales Support
Nutek, Inc. (http://www.rkroy.com ), Email:Customer Support<rkroy@wwnet.net>
Visit www.rkroy.com/wp-txt.html to review the text Design of Experiments Using the Taguchi Approach
: 16 Steps to Product and Process Improvement.
"DOE Demystified" http://www.manufacturingcenter.com/tooling/archives/1202/1202qmdesign.asp
Quick Evaluation Guide (Print this and keep for your own use)
Basic Capabilities (review analysis capabilities first)
1. Run Qualitek-4 (QT4) program from your computer and click OK past the Registration Screen to the experiment
configuration screen. Notice that experiment file PISTON.Q4W is already loaded in memory. If not, OPEN file
PISTON.Q4W.
2. Proceed to Review and Analyze Results of PISTON and other example experiments. There are over 50 example
experiments of all sizes included with the program.
3. Click Analysis menu and select Standard Analysis (either average or stand. deviation). Since PISTON has three
samples/results per trial, QT4 will remind you to perform S/N. Click OK to proceed. In the next screen check Bigger is
Better Quality Characteristic and click OK.
4. Review results and click on Graph if you wish to view it. Click OK when done.
5. While in the Main Effect screen, click Plot to view plots of Main Effect. Once you are in the graphics screen, click the
"<<" or ">>" button at the bottom of the graph to display other factor plots. Click OK to return to Main Effect screen.
6. Click on Interaction button, to display interaction plots between any two factors. Select the Automatic option if you
want QT4 to calculate interaction between all possible factors, even though you may not have thought about all the
interactions or included them in your study. QT4 calculates N (N-1)/2 possible pairs of interactions for N factors and
ranks them by Severity of their presence (Severity Index, 0 - 100%). Review the list to note the important pairs of
interaction for future reference. Click OK or Cancel to return to Main Effect screen and click OK to proceed to ANOVA
screen.
7. In ANOVA screen, try POOLING the factor with least amount of influence (last column). To POOL factors, double
click on the factor description. At prompt, review % Confidence Level and click OK. Notice how the ANOVA table gets
updated. You can also try one or more of the following tasks.
9. Click OK to return to the main screen. From the file menu, click on Open and select any other experiment file from
the list of over 50 files (Open POUND.Q4W if you are interested in reviewing OEC). Analyze results by following steps
3 -8.
11. Automatic Experiment Design If you want Qualitek-4 to select the appropriate orthogonal arrays and design the
experiment, select the Automatic Design option in the DESIGN menu. Qualitek-4 can design experiments for most
common situations. Give this a try.
12. After design is completed, you may review the trial condition by clicking on REVIEW menu and selecting Trial
Condition. Should you want to carry out these experiments, you may print some or all the trial conditions. You may also
save the trial conditions in a file to email/export electronically to distant locations.
(1) Four 2-Level factors, (2) Five 2-Level factors, (3) Six 2-Level factors, (4) Seven 2-Level factors,
(5) Four 2-Level factors + Three interactions, (6) Five 2-Level factors + Two interactions
(7) Six 2-Level factors + One interaction, (8) Four 2-Level factors + One 4-Level factor
(9) Three 2-Level factors + One 4-Level factor, (10) Two 2-Level factors + One 4-Level factor
(11) One 2-Level factors + One 4-Level factor, (12) Four 2-Level factors + One 3-Level factor
(13) Three 2-Level factors + One 3-Level factor, (14) Two 2-Level factors + One 3-Level factor
(15) One 2-Level factors + One 3-Level factor.
The textbooks (www.rkroy.com/wp-txt.html ) by R. Roy corresponds 100% with the software. Consider using one or both
books for your class when you plan to use the software for the class (visit www.rkroy.com/wp-q4w.html for free license).
"If your purpose is to design and analyze Taguchi DOE, nothing beats this package."
"We liked the option to design experiments with mixed level factors. Qualitek-4 seem to offer quite a selection of
arrays."
"The automatic design option in Qualitek-4 is cute. Im glad I dont have to remember how to upgrade and downgrade
columns and how to assign factors The software does it all."
"We found the software to be easy to use. I was able to design L-8 experiment and print trial conditions in matter of
minutes."
"Some of the new features in the Windows XP version great. The option to save trial condition to a file help me send the
information electronically to distant experimental locations."
"Im happy about the many ways of analyzing results and the fact that it instantly transform the S/N prediction in terms
of real units of results."
"The severity index for all possible interactions that we do not included in the experiment, is a feature I have not found
in any other package."
"The process diagram and analysis of DYNAMIC CHARACTERISTICS are streamlined advanced features that are
incredibly easy to follow."
[To test use example DC-AS400.Q4W]
"We find that handling of multiple objective simultaneously is unique with Qualitek-4."[To test use example
POUND.Q4W]
About the Taguchi Methods
As a researcher in Electronic Control Laboratory in Japan, Dr. Taguchi carried out significant
research with DOE techniques in the late 1940's. His effort has been to make this powerful
experimental technique more user-friendly (easy to apply) and apply it to improve the quality of
manufactured products. Dr. Taguchi's standardized version of DOE, popularly known as Taguchi
method or Taguchi approach, was introduced in the USA in the early 1980's. Today it is one of the
most attractive quality building tools used by all types of engineers in manufacturing industries.
The DOE using Taguchi approach can economically satisfy the needs of problem solving and
product/process design optimization projects in the manufacturing industry. By learning and
applying this technique, engineers, scientists, and researchers can significantly reduce the time
required for experimental investigations. DOE can be highly effective when we want to:
Specific Objectives:
• Influence of individual factors on the performance
• Which factor has more influence, which ones have less
• Which factor should have tighter tolerance, which tolerance should be relaxed
• Which factor influences are significant and which are not
• How to allocate quality assurance resources based on objective data
• Whether a supplier’s part causes problems or not (ANOVA data)
• How to combine different factors in their proper settings to get the best results
• How you can substitute a less expensive part to get the same performance
• How much money you can save if you make the design improvement
• How you can determine which factor is causing most of your problems
• How you can set up your process such that it is insensitive to uncontrollable factors
• Which factors have more influence on the mean performance
• What you need to do to reduce performance variation around the target
• How you can adjust factors for a system whose response varies proportional to signal factor
(Dynamic response)
• How to combine multiple criteria of evaluation into a single index
• How you can adjust factor for overall satisfaction of criteria of evaluations
• How the uncontrollable factors affect the performance
• etc.,
Experiment layout:
• High emphasis is put on cost and size of experiments
• Size of experiment for a given number of factors and levels is standardized
• Approach and priority for column assignments are established
• Clear guidelines are available to deal with factors and interactions (interaction tables)
• Uncontrollable factors are formally treated to reduce variation
• Discrete prescriptions for setting up test conditions under uncontrollable factors are
described
• Guidelines for carrying out the experiments and number of samples to be tested are defined
Data analysis:
• Steps for analysis are standardized (main effect, NOVA and Optimum)
• Standard practice for determination of the optimum is recommended
• Guidelines for test of significance and pooling are defined
Interpretation of results:
• Clear guidelines about meaning of error term
• Discrete indicator about confirmation of results (Confidence interval)
• Ability to quantify improvements in terms of dollars (Loss function)
Overall advantage of the Taguchi approach:
DOE using the Taguchi approach attempts to improve quality which is defined as the consistency of
performance. Consistency is achieved when variation is reduced. This can be done by moving the
mean performance to the target as well as by reducing variations around the target. The prime
motivation behind the Taguchi experiment design technique is to achieve reduced variation (also
known as ROBUST DESIGN). This technique, therefore, is focused to attain the desired quality
objectives in all steps. The classical DOE does not specifically address quality .
"The primary problem addressed in classical statistical experiment design is to model the response
of a product or process as a function of many factors called model factors. Factors, called nuisance
factors, which are not included in the model, can also influence the response... The primary problem
addressed in Robust Design is how to reduce the variance of a product's function in the customer's
environment."
-Madhav Phadke, Quality Engineering using Robust Design
To learn more about DOE using Taguchi approach, consider attending or sponsoring our 4-day
Seminar with Application Workshops (www.rkroy.com/wp-s4d.html )
#qt4-020 Frequently Asked Questions
Topic Overviews
1. NEW PHILOSOPHY
- Building quality in the product design.
- Measuring quality by deviation from target (not by rejection).
2. NEW DISCIPLINE
- Complete planning of experiments and evaluation criteria before
conducting experiments.
- Determining a factor's influence by running the complete
experiment.
3. SIMPLER AND STANDARDIZED EXPERIMENT DESIGN FORMAT
- Orthogonal arrays for experimental design.
- Outer array design for robust product design.
- More clear and easier methods for analysis of results.
WHY DO IT?
- Save cost (Reduce warranty, rejection and cost of development).
AREAS OF APPLICATION:
- Analytical simulation (in early stages of design).
- Development testing (in design and development).
- Process development.
- Manufacturing.
- Problem solving in all areas of manufacturing and production.
REFERENCES:
1. SYSTEM OF EXPERIMENTAL DESIGNS - By Genichi Taguchi, UNIPUB
KRAUS INTERNATIONAL PUBLICATIONS, NEW YORK. 1987.
2. ORTHOGONAL ARRAYS AND LINEAR GRAPHS - By Yuin Wu. AMERICAN
SUPPLIER INSTITUTE, Dearborn, Michigan. 1986.
3. TAGUCHI TECHNIQUES FOR QUALITY ENGINEERING - Philip J. Ross,
McGraw Hill Book Company, New York. 1988.
4. A PRIMER ON THE TAGUCHI METHOD - Ranjit K. Roy, Van Nostrand
Reinhold, New York, 1990.
(F02) ==========================
APPLICATION STEPS
The Taguchi method is used to improve the quality of products and processes.
Improved quality results when a higher level of performance is consistently
obtained. The highest possible performance is obtained by determining
the optimum combination of design factors. The consistency of performance
is obtained by making the product/process insensitive to the influence of
the uncontrollable factor. In Taguchi's approach, optimum design is
determined by using design of experiment principles, and consistency of
performance is achieved by carrying out the trial conditions under
the influence of the noise factors.
(F03) ==========================
BRAINSTORMING (F3)
2. FACTORS
- What are the factors that influence the performance criteria?
- Which factors are more important than others?
3. NOISE FACTORS
- Which factors can't be controlled in real life?
- Is the performance dependent on the application environment?
4. FACTOR LEVELS
- What are the ranges of values the factors can assume within
practical limits?
- How many levels of each factor should be used for the study?
- What is the tradeoff for a higher level?
6. SCOPES OF STUDIES
- How many experiments can we run?
- When do we need the results?
- How much does each experiment cost?
7. ADDITIONAL ITEMS
- What do we do with factors that are not included in the study?
- In what order do we run these experiments?
- Who will do these experiments?
etc.
(F04) ==========================
QUALITY CHARACTERISTICS (F4)
(F05) ==========================
FACTORS AND LEVELS (F5)
FACTORS ARE:
* design parameters that influence the performance.
* input that can be controlled.
* included in the study for the purpose of determining their
influence and control upon the most desirable performance.
LEVELS ARE:
* Values that a factor assumes when used in the experiment
Example: As in the above cake baking process the levels for sugar
and flour could be:
2 pounds, 5 pounds, etc. (Continuous level)
type 1, type 2, etc. (Discrete level)
(F06) ==========================
INTERACTION BETWEEN FACTORS (F6)
Interaction:
* is an effect (output) and does not alter the trial condition.
* can be determined even if no column is reserved for it.
* can be fully analyzed by keeping appropriate columns empty.
* affects the optimum condition and the expected result.
(F07) ==========================
NOISE FACTORS AND OUTER ARRAYS (F7)
When trial conditions are repeated without the formal "Outer Array"
design, the noise conditions are considered random.
(F08) ==========================
SCOPE AND SIZE OF EXPERIMENT (F8)
The scope of the study, i.e., cost and time availability, are factors that
help determine the size of the experiment. The number of experiments
that can be accomplished in a given period of time, and the associated costs
are strictly dependent on the type of project under study.
(F09) ==========================
ORDER OF RUNNING EXPERIMENTS (F9)
REPETITION - The most practical way may be to select the trial condition
in random order then complete all repetitions in that trial.
(F10) ==========================
REPETITIONS AND REPLICATIONS (F10)
Example: L-8 inner array and L-4 outer array. 8x4 = 32 samples.
Select a trial condition randomly and complete all 4 samples.
Take the next trial at random and continue.
REPLICATION: Conduct all the trials and repetitions in a completely
randomized order.
(F11) ==========================
AVAILABLE ORTHOGONAL ARRAYS (^F1)
Six arrays from the above list are also available for combining the
noise factors included in the study (OUTER ARRAY).
(F12) ==========================
TRIANGULAR TABLE/LINEAR GRAPHS (^F2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
___________________________________________________________________
(1) 3 2 5 4 7 6 9 8 11 10 13 12 15 14
(2) 1 6 7 4 5 10 11 8 9 14 15 12 13
(3) 7 6 5 4 11 10 9 8 15 14 13 12
(4) 1 2 3 12 13 14 15 8 9 10 11
(5) 3 2 13 12 15 14 9 8 11 10
(6) 1 14 15 12 13 10 11 8 9
(7) 15 14 13 12 11 10 9 8
(8) 1 2 3 4 5 6 7
(9) 3 2 5 4 7 6
(10) 1 6 7 4 5
(11) 7 6 5 4
(12) 1 2 3
(13) 3 2
(14) 1
12345678901234567890123456789012345678901234567890123456789012345678901234567890
1 2 3 4 5 6 7 8 9 10 11 12 13
___________________________________________________________
(1) 3 2 2 6 5 5 9 8 8 12 11 11
4 4 3 7 7 6 10 10 9 13 13 12
(2) 1 1 8 9 10 5 6 7 5 6 7
4 3 11 12 13 11 12 13 8 9 10
(3) 1 9 10 8 7 5 6 6 7 5
2 13 11 12 12 13 11 10 8 9
(4) 10 8 9 6 7 5 7 5 6
12 13 11 13 11 12 9 10 8
(5) 1 1 2 3 4 2 4 3
7 6 11 13 12 8 10 9
(6) 1 4 2 3 3 2 4
5 13 12 11 10 9 8
(7) 3 4 2 4 3 2
(8) 1 1 2 3 4
10 9 5 7 6
(9) 1 4 2 3
8 7 6 5
(10) 3 4 2
6 5 7
(11) 1 1
13 12
(12) 1
11
(F13) ==========================
UPGRADING A COLUMN (^F3)
COLUMN MODIFICATIONS:
1 1 -------> 1
1 2 -------> 2
2 1 -------> 3
2 2 -------> 4
1 1 1 -------> 1
1 1 2 -------> 2
1 2 1 -------> 3
1 2 2 -------> 4
2 1 1 -------> 5
2 1 2 -------> 6
2 2 1 -------> 7
2 2 2 -------> 8
DUMMY TREATMENT
---------------
Example:
(F15) ==========================
RESULTS OF MULTIPLE CRITERIA (^F5)
Assume:
X1 = Numeric evaluation under criterion 1
X1ref = Highest numerical value X1 can assume
Wt1 = Relative weighting of criterion 1
(F16) =======================
MSD AND S/N RATIOS (file: TAG-F16.TXT)
S/N based on variance and mean combines the two effects with target
at 0. The purpose is to increase this ratio ((Vm-Ve)/(nxVe)) and thus
a + sign is used in front of Log() for S/N. Also, since for an arbitrary
target value, (Vm-Ve) may be negative, target=0 is used for calculation of
Vm. Expressions for all types of S/N ratios are shown on the next screen.
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º º
º MSD = ( (Y1-Y0)^2 + (Y2-Y0)^2 + .... (Yn-Y0)^2 )/n for NOMINAL IS BEST º
º Variance: Ve = (SSt - SSm)/(n-1) ................. for NOMINAL IS BEST º
º Variance and Mean = (SSm - Ve)/(n*Ve) (with TARGET=0) " " " " º
º where SSt = Y1^2 + Y2^2 and SSm = (Y1 + Y2 +..)^2/n º
º º
º MSD = ( Y1^2 + Y2^2 + ................... Yn^2 )/n for SMALLER IS BETTER º
º MSD = ( 1/Y1^2 + 1/Y2^2 + ............. 1/Yn^2 )/n for BIGGER IS BETTER º
º º
º S/N = - 10 x Log(MSD)................. for all characteristics º
º S/N = + 10 x Log(Ve or Ve and Mean) .. for NOMINAL IS BEST only. º
º º
º º
º Note: Symbol (^2) indicates the value is SQUARED. º
º º
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DYNAMIC CHARACTERISTIC
(Conduct of experiments and analysis of results)
Reference text: Taguchi Methods by Glen S. Peace, Addison Wesley
Publishing Company, Inc. NY, 1992 (Pages 338-363)
* You will need to describe signal and noise factors and their levels.
You will also have to decide on the number of levels of signal and
noise factors. BUT MOST IMPORTANTLY, you will have to choose
the nature of the ideal function (Straight line representing the
behavior Response vs. Signal) applicable to your system.
Step 4. Strictly follow the prescribe test conditions.
Step 4. Enter results in the order and locations (run#) prescribed
in step 4 using option F1 from analysis menu.
When the signal values are known, zero pint or reference point
proportional should be considered first. If neither is appropriate,
the linear equation should be used.
Notations
* = multiplication, ^ = raised to the power
/ = devision by
SIGNAL FACTOR
Step 7: Repeat calculations for all other trials in the same manner.
12345678901234567890123456789012345678901234567890123456789012345678901234567890
M1 M2 M3
Noise 1 Noise 2 Noise 1 Noise 2 Noise 1 Noise 2
|_______________________|______________________|______________________
Trl#1| 5.2 5.6 5.9 5.8 | 12.3 12.1 12.4 12.5| 22.4 22.6 22.5 22.2
Signal strengths: M1 = 1/3, M2 = 1, M3 = 3
= 10 Log(22.76)
(F17) ==========================
TAGUCHI VS. CLASSICAL DOE (^F7)
GENERAL ATTRIBUTES:
* Standard or "cook book" approach. * Methods are not standardized.
* Smaller number of experiments. * Larger number of experiments.
* Standard method of noise factor * No standardized method of noise
treatment. factor treatment.
* Seeks to find stable condition * Develops models by separating
in the face of error. weak main effects for random error.
* Used to solve engineering probs. * Used to solve scientific experiments.
(F18) ==============================
LOSS FUNCTION (^F8)
The Loss Function offers a way to quantify the improvement from the
optimum design determined from an experimental design study.
Definitions:
(F19) ==============================
COMBINATION DESIGN
------------------
(F20) ==============================
HOW TO DESIGN AND ANALYZE EXPERIMENTS USING QUALITEK-4
HOW TO:
1. DESIGN EXPREIMENTS
- For help with test planning/BRAINSTORMING steps, press [F7]
FROM MAIN MENU. Use arrow keys to scroll up and down.
- Press [F1] to design your own experiment. Select [F1], manual
experiment design option, then select the array of your choice.
Type description of factors and levels. Use arrow keys to move
factors and levels. Press function keys to set INTERACTION and
UNUSED columns. Press F2 when done
- If you like QUALITEK-4 to select the array and assign factors,
then select M-F1-F4 for automatic design option. Follow screen
prompt to complete design.
4. PREPARE REPORTS
- Be in the OPTIMUM SCREEN to select options for report and
summary observations.
- Press [F6] to prepare presentation quality report. Review
summary report for management. Select complete report option.
Follow screen print and proceed to print a sample report if
printer is available. If you experience printer problem, return
to main menu and reset printer file data.
- Return to analysis menu when done printing report.
- If you wish to print graphs, be in the graphical display scree.
While the graph is diplayed, press print options as indicated.
- Review more example experiments from over 40 experiment files
included with the program.
QUICK REFERENCE
(Notations: M-Main menu, A-Analysis menu, F1-Function key,etc.)
Areas of Specialization:
- Design of Experiment(DOE) Using Taguchi Approach( Seminar, Software & Consulting
- Reliability Test Planning and Evaluation (Workshop & Software)
- Structured Problem Solving (Consulting)
- Statistical Process Control
What we do best:
At NUTEK, we're quality engineering specialists. It's our business to solve quality-related
production problems, and to teach clients how to do the same. Our main focus is on real-world
applications, not theory. We've taken the methods developed by pioneers in the field and
streamlined them to fit the practical needs of American manufacturers today.
Our training seminars on Design of Experiments(DOE) using the Taguchi approach, provide an
immediate working knowledge of quality building techniques. Always application-oriented, our
teaching is aimed at helping your engineering, manufacturing and production specialists implement
quality improvement plans from day one.<P>
Our user-friendly QUALITEK-4 software automates Taguchi experiment layout and performs
complex calculations in a matter of seconds. It also analyzes results, and makes recommendations
for product and process design improvements
Our consulting services offer immediate solutions to difficult and persistent problems. We work
with your project teams to plan experiments, analyze results, and make recommendations for
improvements. Our comprehensive application consulting services support clients in the following
three phases of the projects:
Troubleshooting =====================================