Robust Design
(Taguchi Method)
Background,
• Robust Design method aka Taguchi Method, pioneered
by Dr. Genichi Taguchi, greatly improves engineering
productivity.
• By consciously considering the noise factors
(environmental variation during the product's usage,
manufacturing variation, and component deterioration)
and the cost of failure in the field the Robust Design
method helps ensure customer satisfaction.
• Robust Design focuses on improving the fundamental
function of the product or process, thus facilitating
flexible designs and concurrent engineering. Indeed, it is
the most powerful method available to reduce product
cost, improve quality, and simultaneously reduce
development interval.
Why Use Robust Design Method?
• Over the last five years many leading companies have invested
heavily in the Six Sigma approach, aimed at reducing waste during
manufacturing and operations.  great impact on the cost structure
and hence on the bottom line of those companies.?
• Brenda Reichelderfer of ITT Industries reported on their
benchmarking survey of many leading companies
– "design directly influences > 70% of the product life cycle cost;
companies with high product development effectiveness have earnings
three times the average earnings; and companies with high product
development effectiveness have revenue growth two times the average
revenue growth."
– "40% of product development costs are wasted!"
• The Design for Six Sigma approach is focused on:
1) increasing engineering productivity so that new products can be
developed rapidly and at low cost
2) value based management.
• Many companies around the world have saved hundreds of millions
of dollars by using the method in diverse industries: automobiles,
xerography, telecommunications, electronics, software, etc.
Typical Problems Addressed By Robust
Design,
A team of engineers was working on the design
of a radio receiver for ground to aircraft
communication requiring high reliability, i.e.,
low bit error rate, for data transmission
– On the one hand, building series of prototypes to
sequentially eliminate problems would be forbiddingly
expensive.
– On the other hand, computer simulation effort for
evaluating a single design was also time consuming
and expensive. Then, how can one speed up
development and yet assure reliability?
In an another project,
• A manufacturer had introduced a high speed copy
machine to the field only to find that the paper feeder
jammed almost ten times more frequently than what was
planned. The traditional method for evaluating the
reliability of a single new design idea used to take
several weeks. How can the company conduct the
needed research in a short time and come up with a
design that would not embarrass the company again in
the field?
• The Robust Design method has helped reduce the
development time and cost by a factor of two or
better in many such problems.
Two categories of engineering decisions,
• In general, in product/system development 2 categories
of engineering decisions are:
– Error-free implementation of the past collective knowledge and
experience
– Generation of new design information, often for improving
product quality/reliability, performance, and cost.
• While CAD/CAE tools are effective for implementing past
knowledge, Robust Design method greatly improves
productivity in generation of new knowledge by acting as
an amplifier of engineering skills.
• With Robust Design, a company can rapidly achieve the
full technological potential of their design ideas and
achieve higher profits.
Robustness Strategy
• Variation reduction is universally recognized as a key
to reliability and productivity improvement. There are
many approaches to reducing the variability, each one
having its place in the product development cycle.
• By addressing variation reduction at a particular stage in
a product's life cycle, one can prevent failures in the
downstream stages. The robustness strategy is to
prevent problems through optimizing product designs
and manufacturing process designs.
Case example
• The manufacturer of a differential op-amplifier
used in coin telephones faced the problem of
excessive offset voltage due to manufacturing
variability. High offset voltage caused poor voice
quality, especially for phones further away from
the central office. So, how to minimize field
problems and associated cost? There are many
approaches:
1. Compensate the customers for their losses.
2. Screen out circuits having large offset voltage at the end
of the production line.
3. Institute tighter tolerances through process control on
the manufacturing line.
4. Change the nominal values of critical circuit parameters
such that the circuit's function becomes insensitive to
the cause, namely, manufacturing variation.
Answer: No 4
• The approach 4 is the robustness strategy. As one
moves from approach 1 to 4, one progressively moves
upstream in the product delivery cycle and also becomes
more efficient in cost control. Hence it is preferable to
address the problem as upstream as possible. The
robustness strategy provides the crucial methodology for
systematically arriving at solutions that make designs
less sensitive to various causes of variation. It can be
used for optimizing product design as well as for
manufacturing process design.
The Robustness Strategy uses 5 primary
tools:
1. P-Diagram is used to classify the variables associated with the
product into noise, control, signal (input), and response (output)
factors.
2. Ideal Function is used to mathematically specify the ideal form
of the signal-response relationship as embodied by the design
concept for making the higher-level system work perfectly.
3. Quadratic Loss Function (also known as Quality Loss
Function) is used to quantify the loss incurred by the user due to
deviation from target performance.
4. Signal-to-Noise Ratio is used for predicting the field quality
through laboratory experiments.
5. Orthogonal Arrays are used for gathering dependable
information about control factors (design parameters) with a
small number of experiments.
Design of Experiments
•
•
•
A robust product is one that works as intended regardless of variation in a
product's manufacturing process, variation resulting from deterioration, and
variation in use.
Robust design can be achieved when the designer understands these
potential sources of variation and takes steps to desensitize the product to
these potential sources of variation.
Robust design can be achieved through "brute force" techniques of added
design margin or tighter tolerances or through "intelligent design" by
understanding which product and process design parameters are critical to
the achievement of a performance characteristic and what are the optimum
values to both achieve the performance characteristic and minimize
variation.
When the operation of the product or achievement of a performance
characteristic can be mathematically related to a product or process design
parameter, optimum product and process design parameters can be
calculated. When these relationships are unknown, design of experiments
(DOE) can aid in determining these optimum parameter values and,
thereby, developing a more robust design.
Design of Experiments Description
•
•
Design of Experiments is based on the objective of desensitizing a product's
performance characteristic(s) to variation in critical product and process
design parameters.
Genichi Taguchi developed the concept of "loss to society". In this concept,
variability in critical design parameters will increase the loss to society which
is an expanded view of the traditional, internally-oriented cost of quality. This
is a quadratic relationship of increasing costs (loss to society) as these
critical design parameter values vary from the desired mean value of the
parameter.
To consider quality implications during design, the design process can be
segmented into three stages.
1. The first stage, system design, establishes the functionality of the product, the
physical product envelope, and general specifications.
2. The second stage, parameter design, establishes specific values for design
parameters related to physical and functional specifications. It is during these
first two stages that the designer has the greatest opportunity to reduce product
costs through effective functional design and parameter specification.
3. The third stage, tolerance design, establishes the acceptable tolerances around
each parameter or target. The third stage typically will add costs to the product
through efforts to ensure compliance with the tolerances associated with product
parameters.
Design of Experiments Description...cont’d
•
Since an organization cannot cost-effectively inspect quality into the product, it
must focus on minimizing variability in the product through product and
process design and control of processes. However, some variability is
uncontrollable or very difficult to control. This difficult to control variation is referred to
as noise. Noise is the result of variation in materials, processes, the environment and
the product's use or misuse. Products need to be designed so that they are robust their performance is insensitive to this naturally occurring, difficult to control variation.
Design of Experiments techniques provide an approach to efficiently designing
industrial experiments which will improve the understanding of the relationship
between product and process parameters and the desired performance
characteristic. This efficient design of experiments is based on a fractional factorial
experiment which allows an experiment to be conducted with only a fraction of all the
possible experimental combinations of parameter values. Orthogonal arrays are used
to aid in the design of an experiment. The orthogonal array will specify the test cases
to conduct the experiment. Frequently, two orthogonal arrays are used: a design
factor matrix and a noise factor matrix, the latter used to conduct the experiment is
the presence of difficult to control variation so as to develop a robust design. This
approach to designing and conducting an experiment to determine the effect of
design factors (parameters) and noise factors on a performance characteristic is
represented below.
Design of Experiments Description...cont’d
•
These experimental results can be summarized into a metric called the signal to
noise ratio which jointly considers how effectively the mean value (signal) of the
parameter has been achieved and the amount of variability that has been
experienced. As a result, a designer can identify the parameters that will have the
greatest effect on the achievement of a product's performance characteristic.
The design parameters or factors of concern are identified in an inner array or design
factor matrix which specifies the factor level or design parameter test cases. The
outer array or noise factor matrix specifies the noise factor or the range of variation
the product will be exposed to in the manufacturing process, the environment or how
the product used (conditions it is exposed to). This experimental set-up allows the
identification of the design parameter values or factor levels that will produce the best
performing, most reliable, or most satisfactory product over the expected range of
noise factors or environmental conditions.
After the experiments are conducted and the signal to noise ratio determined for each
design factor test case, a mean signal to noise ratio value is calculated for each
design factor level or value. This data is statistically analyzed using analysis of
variation (ANOVA) techniques. Very simply, a design factor with a large difference in
the signal noise ratio from one factor setting to another indicates that the factor or
design parameter is a significant contributor to the achievement of the performance
characteristic. When there is little difference in the signal to noise ratio from one
factor setting to another, this indicates that the factor is insignificant with respect to
the performance characteristic.
Design of Experiments Description...cont’d
• From the result, the designer can:
– Identify parameter values which maximize achievement of
performance characteristic and minimize the effect of noise,
thereby achieving a more robust design.
– Identify parameters that have no significant effect on
performance. In these cases, tolerances can be relaxed and
cost reduced.
– Identify parameter values which reduce cost without affecting
performance or variation.
• These steps take initial effort, but can reduce cost and
improve the performance of the product.
Variability Reduction
• Variability Reduction is a multipart strategy to reduce product
variation and make a product
more robust or fit to use, e.g.,
meet its performance
requirements regardless of
variation.
1. Broadly speaking, there are
three sources of variation:
manufacturing variation
2. environmental/deterioration
variation
3. usage variation.
Variability reduction involves understanding customer needs and
developing a product and process design that balances these needs
with process capabilities and potential sources of variation. Thus
variability reduction is broader than SPC and DOE individually and
more proactive than SPC. The following diagram represents an
overall framework for variability reduction or quality engineering.
In the real field,