| Nutek, Inc. Quality Engineering Seminar, Software, and Consulting ( Since 1987) Site: Dynamic System |
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Robust Product and Process Designs with Dynamic Characteristics This session is for specialist in engineering design, research and development who have advance engineering thinking and wish to proactively initiate or mentor the organization in efforts to robust product and process designs.
How to schedule
Course materials Who
should attend? |
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Our onsite seminar/workshop offers your project team the proven skill they need to tackle the problems on the production floor and build robustness in product or process designs. The attendees to these sessions learn how to apply the Taguchi experimental design technique to (1) Optimizing product/process designs, and (2) Solve production problems . |
How to schedule: Classes are generally scheduled 12 weeks in advance. Call or write us (
1-248-812-2071 ) for a formal cost proposal in electronic or hardcopy format.Session Format: The 5-day seminar can also be split into a 3-day and a 2-day sessions when attendees have projects for immediate applications.
Course Materials: As a session sponsor you will receive our handout materials consist of approximately over 250 pages. You may duplicate these materials for use during this and any future session at your facility. You will also receive one complimentary copy of our Qualitek-4 software (1 user/installation). Optionally, you may consider purchasing the textbooks by the instructor to the workshop participants.
ABOUT THE TECHNIQUE - Taguchi experiment design technique allows simultaneous study of multiple factors which influence the performance. It is an experimental strategy in which the number of experiments is minimum and the method for analysis of results is standardized. Most often the experiments are done using experimental hardware. But it can also be applied to analytical simulation if available. When applied to product or process optimization, the technique helps improve consistency of performance. Reducing variations can solve most production problems that are caused by higher rejects and warranty. The robust design strategies for products or processes with dynamic characteristics have the potential for most cost savings in the long term.
In this application-oriented seminar, emphasis is put on teaching Taguchi philosophy and his experimental methodologies rather than the math. The attendees learn the basic application techniques of Taguchi experimental design. The course teaches application technique through actual case studies. Some theory and analysis techniques are covered, as well as methods for accomplishing major analysis tasks using computer software.
The last two days of the session are dedicated to helping attendees learn how to apply and analyze the results using Qualitek-4 computer software. All attendees are encouraged to bring their own projects to the class and when possible attend the seminar as a group with other project team members. The workshop portion (last two days) of the session allows attendees to learn applications with hands-on exercises. All software necessary for the conduct of the workshop is provided by the instructor.
WHO SHOULD ATTEND?
This session is designed to prepare the attendees for immediate applications.
It covers time-proven methodologies and takes attendees through hands-on
application exercises related to setting up experiments and analysis procedures.
Attend if you wish to learn how to apply yourself confidently, or send the
potential mentor or local expert within your organization. If your involve one
or more of the following activities, you would benefit from this
seminar/workshop.
By completing this session and applying the technique, you will expect to:
WHAT ARE THE TEACHING METHODS?
Communicating the application philosophy and basic techniques are the main objectives. The emphasis is on showing how it is applied, rather than teaching the theory and the math. Practical application methods including brainstorming session for application case studies are discussed in details.DETAILED COURSE DESCRIPTION
Overview of Taguchi
concepts of quality engineering
Understanding what Design of Experiment (DOE) is and what’s
new with the Taguchi approach is essential for developing application expertise.
Although, detail statistical theory of DOE is beyond the scope of this seminar,
basic principles of DOE and the standardized steps practiced today are covered
in detail. You will understand: - How quality is defined as consistency and the statistical
terms used to measure it - Standardization introduced by Taguchi - How a team approach can be beneficial to application effort
Measuring cost of
quality by Loss Function
Conventionally, the cost of lack of quality is measured by cost of rejection
at production. In the Loss Function, the ill effects of poor quality when the
product is in use, is included in the cost. You will learn how the Loss Function
is used to quantify the dollar benefits from improved designs.
Review basic
concepts in experimental design
Experimental designs for simultaneous study of multiple two
level factors introduces the basic principles in DOE. Types of factors, effects
of factor level on the size of experiments, and the desired qualities of the
orthogonal arrays are discussed in detail. You will understand: - Types of factors - Multiplicity of levels - Orthogonal array vs. one factor at a time experiments
Project objective
and Overall Evaluation Criteria
When project objectives are numerous, it is likely that they
are measured using subjective as well as objective evaluation criteria with
different units of measurements. When the goal is to seek a design that is
overall best, it becomes necessary to combine all evaluation criteria into a
single number. A popular scheme is to define an Overall Evaluation Criterion (OEC).
You will get an insight into: - Why it is difficult to combine engineering measurements
into a single quantity - What is the need for combining multiple evaluation criteria
into a single index - Rationale behind the scheme to combine subjective and
objective evaluations into one number
Experiments designed
using orthogonal arrays
Orthogonal arrays present opportunities to handle numerous
experimental situations using a few simple guidelines. A small number of arrays,
each of which can be used for multiple experimental situations, can also be used
for: - Experiments with all factors having two, three or four
levels - Experiments involving factors at mixed level - Example analysis for Main Effect and Optimum Condition.
Experiments to study
interaction
Basic analysis and
strategy for experimentation
Compromising what you would like to study and what you can
afford to study is a key decision in planning the experiment. Because
interaction between factors under investigation is inevitable, and since the
size of the experiment becomes prohibitively large when all interactions are
included in the study, a balance between the number of factors and the
interaction becomes extremely important. Knowledge about how to test for
presence of interaction allows you to make a practical compromise between the
two.
Experiments with
mixed level factors
Factors selected for studies may not all have the same number
of levels. Mixed levels (2, 3 and 4) factors can often be handled by using the
standard 2-level arrays such as L-8, L-16 and L-32. To enhance your experiment
design capabilities, you will learn how to: - Upgrade 2-level columns into a 4-level column - Downgrade (dummy treatment) a 4-level column into a 3-level
column - Downgrade (dummy treatment) a 3-level column into a 2-level
column - Design 15 different experiments using an L-8 array.
Combination Design
(special design tool)
In some experimental situation, conventional design
approach makes the experiment too large; some special technique could
potentially save experiments and time. Combination Design is such a special
technique.
Strategy for Robust
Designs
Variation in sample performances is due to uncontrollable, or
"Noise" factors. To reduce variation is to minimize the influence of the
uncontrollable factors. Conventional approach has been to investigate the
uncontrollable factors and attempt to control their influence. But Taguchi
offers revolutionary Robust Design concept which, instead of going after the
uncontrollable factors, attempts to reduce their influence by adjusting the
controllable factors. As part of this new experiment design strategy, you will
learn: - New attitudes toward uncontrollable factors - How to design an experiment with Outer array - How to achieve more "bang for the buck" from your
experiment.
Experiment layout
for Systems with Dynamic Response
(if time is available)
Although common DOE projects under investigations are static
in nature, there are some that exhibit response, which changes (Dynamic)
depending on a specific factor (Signal). A quick review of the topic is intended
to provide understanding of: - The nature of dynamic systems - How the response characteristics are determined - Procedure for carrying out the experiments.
Analysis of Results
Although detail analysis of less importance in this seminar,
complete statistical calculations are presented through five separate example
experiments. Review of analysis steps through these examples will provide better
understanding of the: - Main effect study for influence of factors - ANOVA for relative influence of factors - Optimum condition for estimation of performance improvement - Cost savings expected from the improvement - Conservative predictions - Confidence level and confidence interval (C.I.)
- Transformation of S/N data into measured units.
Brainstorming for
experimental designs
Although, experiment planning is the first step in the
application process, its full value cannot be realized until one is completely
familiar with the experiment design and analysis techniques. Thus a final
summary of the planning process is presented to reaffirm: - the role of the new disciplines in the workplace - Order of discussions in the planning session - Considerations for selecting participants for the planning session.
Computation of
Savings using the LOSS FUNCTION
Consistent with the intent of the project, reduction of
variation is the primary improvement desired. When analysis is performed using
Signal to Noise Ratio (S/N) of the results, quantified improvement in terms of
variation reduction can be computed. More than variation reduction, management
generally value information in terms of cost benefit (return on investment)
achievable from the improved design.
Strategy for Robust
Designs
Variation in sample performances is due to uncontrollable, or
"Noise" factors. To reduce variation is to minimize the influence of the
uncontrollable factors. Conventional approach has been to investigate the
uncontrollable factors and attempt to control their influence. But Taguchi
offers revolutionary Robust Design concept which, instead of going after the
uncontrollable factors, attempts to reduce their influence by adjusting the
controllable factors. As part of this new experiment design strategy, you will
learn: - New attitudes toward uncontrollable factors - How to design an experiment with Outer array - How to achieve more "bang for the buck" from your
experiment.
Experiment layout
for Systems with Dynamic Response
(if time is available)
Although common DOE projects under investigations are static
in nature, there are some that exhibit response, which changes (Dynamic)
depending on a specific factor (Signal). A quick review of the topic is intended
to provide understanding of: - The nature of dynamic systems - How the response characteristics are determined - Procedure for carrying out the experiments
Analysis of Results
Although detail analysis of less importance in this seminar,
complete statistical calculations are presented through five separate example
experiments. Review of analysis steps through these examples will provide better
understanding of the: - Main effect study for influence of factors - ANOVA for relative influence of factors - Optimum condition for estimation of performance improvement - Cost savings expected from the improvement - Conservative predictions - Confidence level and confidence interval (C.I.)
- Transformation of S/N data into measured units.
Dealing with Dynamic Characteristics
Many products and processes do not have a fixed level of performance or a
target. Instead, such desired results are expected to be in proportion to a key
input (signal) factor. Such responses are said to posses dynamic characteristic.
A common examples is the design of an electrical transformer, say, to step down
voltage by 50% which reduces voltage in a ratio of 2:1 no matter what the input
voltage is. Another example will be the mechanism for a weighing scale which
will show the reading of a subject weight regardless of its magnitude. The
materials covered in this part of the seminar covers strategies for designing
robust products and processes with dynamic characteristics.
Design and analysis
using computer software
Attendees will work as a group to apply the technique in
their own project. The steps involved in real life applications, from experiment
planning to run confirmation tests are traced. For the sake of class projects,
results are assumed and analysis performed as if they were real results. All
attendees learn how to analyze results using Qualitek-4 software. Instructor
provides the software for the class.
YOUR LEARNING OBJECTIVES
REFERENCE TEXT FOR THE SEMINAR
I. A Primer on the Taguchi Method
by
Ranjit Roy,
Hardcover - 247 pages 1 edition
(Available from
WWW.AMAZON.COM
)
ROOM AND FACILITIES FOR THE CLASS
BACKGROUND REQUIREMENTS
TRAINING MATERIALS
SEMINAR INSTRUCTOR
Dr. Roy began his career with The Burroughs Corporation following the completion of graduate studies in engineering at the University of Missouri-Rolla in 1972. He then worked for General Motors Corp. (1976-1987) assuming various engineering responsibilities, his last position being that of reliability manager. While at GM, he consulted on a large number of documented Taguchi case studies of significant cost savings.
Dr. Roy established his own consulting company, Nutek, Inc. in 1987 and currently offers consulting, training, and application workshops in the use of design of experiments using the Taguchi approach. He is the author of
A PRIMER ON THE TAGUCHI METHOD - published by the Society of Manufacturing Engineers in Dearborn, Michigan and Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement published (January 2001) by John Wiley & Sons, New York. Dr. Roy is a fellow of the American Society for Quality and an adjunct professor at Oakland University, Rochester, Michigan. Dr. Roy is listed in the Marquis Who’s Who in the world.Seminar Schedule and Discussion Topics
EQUIPMENT AND COMPUTER NEEDS FOR THE SEMINAR
Is
5-day Seminar with Workshop
right for You?
If majority of your attendees
have 4 or more years of college, they would
benefit from detailed methodologies. If you have participants who hope to apply
immediately, are interested in Robust Design strategies, and expect to have
fairly larger projects, then you should consider
our 5-day seminar. Be aware that, this
sessions include hands-on computer exercise, homework and group project application.
The attendees must be willing to work as groups and carry out a number of
assignments.
If most your prospective attendees prefer simpler applications, are not interested yet about Robust Design, but hope to apply the technique in production problem solving applications, then you should consider our 2-day production problem solving seminar
WHY YOU SHOULD CONSIDER OUR ON-SITE SEMINAR
BACKGROUND OF THE
TAGUCHI METHODQuality as customers perceive it, has many elements such as PERFORMANCE, DURABILITY, RELIABILITY, SERVICE, DELIVERY, etc. Among these quality elements which are directly influenced by the engineering activities, most important is the performance. Use of Taguchi technique attempts to improve consistency in the performance. For most products, improved consistency goes a long way to improve the customer perception of the performance. Thus Taguchi method is generally credited with improving quality when consistency is indeed improved.
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