Transcript ROBUST DESIGN

ROBUST DESIGN
Dr. Genichi Taguchi
Widely acknowledged leader in the U.S. industrial
quality movement
Credited for starting the “Robust Design”
movement in Japan in 1950’s
Helped correct postwar Japan's telephone system
1980 introduced Taguchi method to AT&T
Taguchi said Ideal Quality is delivered when “a
product or service performs its intended function
throughout its projected life under reasonable
operating conditions without harmful side
effects.”
Cont.
Taguchi’s contributions include:
1.The Taguchi loss function ( This
involves the quality lost during the
process )
2.Robust design ( creating an
insensitive
design
towards
uncontrollable variables
3.Off-line and on-line quality control
Robust Design
It focuses on improving the fundamental function
of the product or process, thus facilitating
flexible designs and concurrent engineering to
minimally impacted by external forces, such as
environment,operating,
or
manufacturing
conditions.
It is the most powerful method available to reduce
product cost, improve quality, and simultaneously
reduce development interval
It used in diverse industries: automobiles,
xerography,
telecommunications,
electronics,
software, etc
Steps In Creating Robust Design
Step 1 : Problem Formulation
Step 2 : Data Collection/Simulation
Step 3 : Factor Effects Analysis
Step 4 : Prediction/Confirmation
Steps In Creating Robust Design (cont.)
Step 1 : Problem Formulating
This Steps Include :
1. Developing P-Diagram
2. Defining Ideal Function and S/N Ratio.
3. Planning Experiments
Step 1 (Cont.) :
1.Parameter Diagram (P-Diagram)
It used to identify signal (input), response (output),
noise factor (factors that are beyond the control of
the design), control factors (factors that can be
specified by the designer). Noise factors may include:
– parameter variations
– environmental changes
– operating conditions
– manufacturing variations
P-Diagram (Cont.)
Example : Brownie Mix
• Controllable Input Parameters
– Recipe Ingredients (quantity of eggs, flour,
chocolate)
– Recipe Directions (mixing, baking, cooling)
– Equipment (bowls, pans, oven)
• Uncontrollable Noise Factors
– Quality of Ingredients (size of eggs, type of oil)
– Following Directions (stirring time, measuring)
– Equipment Variations (pan shape, oven temp)
• Measurable Performance Response
– Taste Testing by Customers
– Sweetness, Moisture, Density
Step 1 (cont.) :
2. Ideal Function & S/N Ratio
The ideal function is a function that govern all
engineering system, it’s graph represents the
performance target for the engineered system
Define an objective function (of the response) to optimize.
–
–
–
–
maximize desired performance
minimize variations
quadratic loss
signal-to-noise ratio
Ideal Function & S/N Ratio (cont.)
The general form of ideal function is given
below.
y=bM
where, y = the output response, M = input signal
and b = slope of the line.
Sensitivity
The slope b of the ideal function equation is
called sensitivity. Though higher sensitivity is
desired, one has to be careful because higher
sensitivity sometimes may cause problems.
Ideal Function & S/N Ratio (cont.)
Noise factors are parameters that are
uncontrollable, or have high impact on the cost.
Ex : Driving habits, road conditions, temperature,
humidity, deterioration etc.
And signal is a variable that user expects to have
a certain change in the output response as
designed to. Ex : Force applied on brake pressure,
cutting force, rotation of steering wheel etc.
Ideal Function & S/N Ratio (cont.)
S/N =
(Intended output) / (Unintended output)
= (What we want) / (What we don't want)
= (Useful energy) / (Waste energy)
The purpose is to optimize the S/N
ratio according to the situation that
is involved.
Type Of S/N Ratio :
(a) Smaller-the-better
f(y) = 1/y2
For example : variance, the number of flaws in the paint
on an automobile
(b) Nominal-the-best
f(y) = 10 * log10 (Mean2/Variance)
This signal-to-noise ratio could be used whenever ideal
quality is equated with a particular nominal value. For
example: target, the size of piston rings for an autotomobile
engine must be as close to specification as possible to
ensure high quality.
Type Of S/N Ratio (cont.) :
(c) Larger-the-better
f(y) = y2
Examples of this type of engineering problem are
performance, fuel economy (miles per gallon) of an
automobile,strength of concrete, resistance of
shielding materials, etc.
(d) Signed Target
Eta = -10 * log10(variance)
for i = 1 to no. vars
variance of the quality characteristic across the
measurements (variables). Used when the quality
characteristic of interest has an ideal value of zero.
Type Of S/N Ratio (cont.) :
e) Fraction Defective
Eta = -10 * log10[p/(1-p)]
where p is the proportion defective
This S/N ratio is useful for minimizing scrap,
minimizing the percent of patients who develop
side-effects to a drug, etc.
Step 1 (cont.) :
3. Planning the Experiment.
• Vary the input and noise parameters
• Record the output response
• Compute the objective function
Goals for Planning Experiments :
a. Understanding relationships between design
parameters and product performance
b. Understanding effects of noise factors
c. Reducing product or process variations
Steps In Creating Robust Design (cont.)
Step 2 : Data Collection / Simulation
The Experiments may be conducted in hardware or
simulation. Its more desireable to have an essential model of
the product that adequately captures the disegn concept.
Thus, the experiments can e done more economically
Step 3 : Factor Effects Analysis
In this step, the effect of the control factor are
calculated and the result are analyzed to select optimum
setting of the control factors.
Steps In Creating Robust Design (cont.)
Step 4 : Prediction / Confirmation
To validate the optimum condition we predict
the performance of the product/process design
under baseline and optimum settings of the control
factors. Then we perform confirmation Then we
perform confirmation experiments under these
conditions and compare the results with the
predictions. If the results of confirmation
experiments agree with the predictions, then we
implement the results. Otherwise, the above steps
must be iterated
Robust Design enables engineers to
:
- Develop products and processes which perform
consistently as intended under a wide range of
user's conditions throughout their life cycle
(durable and reliable)
- Maximize
robustness-improve
the
intended
function of the product by developing and
increasing insensitivity to noise factors which tend
to degrade performance
- Develop or change product formulas and process
settings to achieve desired performance at the
lowest cost and in the shortest time
- Simplify designs and processes to reduce cost
References :
Taguchi, Genichiand Clausing, Don
“Robust Quality” Harvard Business Review, Jan-Feb 1990. Byrne, Diane M. and
Taguchi, Shin
“The Taguchi Approach to Parameter Design” Quality Progress, Dec 1987.
Phadke, MadhavS. Quality Engineering Using Robust Design Prentice Hall,
Englewood Cliffs, 1989.
Ross, Phillip J. Taguchi Techniques for Quality Engineering McGraw-Hill, New
York, 1988.
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