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Robust Design (Taguchi Method)

What is robust product design?

Robust product design is a concept from the teachings of Dr. Genichi Taguchi, aJapanese quality guru. It is defined as reducing variation in a product without

eliminating the causes of the variation. In other words, maing the product or process

insensitive to variation. This variation !sometimes called noise" can come from a

variety of factors and can be classified into three main types# internal variation,

e$ternal variation, and unit to unit variation. Internal variation is due to deterioration

such as the wear of a machine, and aging of materials. %$ternal variation is from

factor relating to environmental conditions such as temperature, humidity and dust.

&nit to &nit variation is variations between parts due to variations in material,

 processes and equipment. !'ochner and (atar, )*". %$amples of robust design includeumbrella fabric that will not deteriorate when e$posed to varying environments

!e$ternal variation", food products that have long shelf lives !internal variation", and

replacement parts that will fit properly !unit to unit variation". The goal of robust

design is to come up with a way to mae the final product consistent when the process

is sub+ect to a variety of noise.

How do you make a design robust?

Taguchi considers maing a design robust in the parameter design portion of product

or process design. In parameter design the goal is to find values for controllable

settings that minimi-e the negative effects of the uncontrollable settings. %$periments

are used to determine the impact of particular settings on both the controllable and

uncontrollable factors. The idea here is that by observing changes in a controllable

factor !such as the thicness of boards", a value can be found for that factor that

reduces the effect !warping" of something that cant be controlled !the humidity

outside". The ultimate goal is to find the optimal settings to minimi-e cost by

minimi-ing variation.

/hen setting up these e$periments, the factors that effect the product need to be

determined. Then the factors can be separated into controllable factors and

uncontrollable factors and e$periments can be set up to test the effects of changing the

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values of each factor. There are many ways to set up these e$periments. Taguchis

method involves finding correlation between variables. 0e uses orthogonal arrays,

with the inner array consisting of control factors and the outer array consisting of 

noise factors. %ach inner array is to be run with each outer array. !If si$ control

factor e$periments and three noise factor e$periments are needed, there will have to be !si$ times three" eighteen e$perimental trials to get all the combinations". 1nother 

method for conducting these e$periments is to mae no attempt to control the noise

factors, but repeatedly run the trials for combinations of control factors. !'ochner and

(atar, )23" This type of e$periment allows the operator to measure process

variability. The trials should be taen in an environment similar to the one in which

the actual use or manufacturing of the product is going to tae place. 1 third

e$perimental design is to identify all the control and noise factors !adding the

control and noise factors yields " and run an analysis using at least 4) trials based

on eight5run e$periments. !6ou could use an eight run e$periment for up to 78, and a

si$teen run e$periment for up to 7)2." This will allow the interaction between

variable to be seen running fewer tests than using Taguchi9s method. :urther 

instruction as to how to use this method is found in chapter four of Designing for 

;uality by 'ochner and (atar.

The data found from the e$perimental trials is then analy-ed. The analysis will depend

on the method of e$perimentation. y performing a series of replica

e$periments at the levels that were piced, we can see if the values achieved matched

that of the values the model predicted. If there is disparity, there may be an interaction

or noise that we didnt see and thus our e$periment must be redeveloped.

What are the advantages of robust design?

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Robust design has many advantages. :or one, the effect of robustness on quality is

great. Robustness reduces variation in parts by reducing the effects of uncontrollable

variation. (ore consistent parts equals better quality.

1nother advantage is that lower quality parts or parts with higher tolerances can beused and a quality product can still be made. This saves the company money, because

the less variable the parts can be the more they cost.

1 third advantage is that the product will have more appeal to the customer.

?ustomers demand a robust product that won9t be as vulnerable to deterioration and

can be used in a variety of situations.

This method is also good, because you are designing the robustness into the product

and process instead of trying to fi$ variation problem after they occur.

What are the disadvantages of robust design?

@ne of the disadvantages of robust design is that to effectively deal with the noise, the

designer must be aware of the noise. If there is a noise factor that is affecting the

 product and the e$periments run do not address it !intentionally or not", the only way

that the product will be robust to that variation is by luc.

1nother disadvantage to robust design done Taguchis way is that the problem

 becomes large quicly. If you had a lot of different things to consider as control

variables and=or noise variables, it would tae a great deal of time to run all the

e$perimental trials. ?ontrolling noise variables is e$pense, and when lots of trials are

required the dollars add up.

1nother disadvantage is that by using orthogonal arrays, it assumes the noise factors

are independent, which may be helpful in setting up the e$periment, but is not

necessarily a good assumption !'ochner and (atar, )2A".

What are some eamp!es of why robust design is important?

?onsider this e$ample adapted from ?reating ;uality by BolariC the designers of a

radio had built and tested a breadboard. 1fter the radio was considered a success, the

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specifications were passed to production and the radios began being manufactured.

The first production units radios went into test and failed to meet maretings product

 performance requirements, as did the second unit. 1nalysis of why the process failed

 produced no results. They had been following procedure and using standard

acceptable parts. e$t the breadboard of the original design was inspected. It wasfound that the designers had hand5tested and piced all the component parts. They

wored much better than the manufacturers standard acceptable parts. 1fter review of 

the design it was found that there was no way to economically fi$ the problem without

massive redesign, so personnel were assigned the tas of manually sorting the

components, costing the company additional time.

In this e$ample the design of the radio needed to be robust so that it could handle the

amount of variation in the set of standard acceptable parts. >ecause the design didnt

allow for that amount of variability, it cost the company lost time. They had to stopthe production process and investigate and then they had to e$pend further manpower 

in screening the parts.

(aing a product robust is also a concern for companies that manufacture products

for an ever5e$panding maret. If products are sold nation wide or even globally, the

differences in the environments, conditions, and uses have to be considered for them

to be a success. :or e$ample, a manufacturer of a certain type of gas grill that is sold

nationally must consider the robustness of the materials used to mae the grill. The

 people in (innesota may use the grill in the summer only and it is stored in the garage

in the winter where the temperature falls to free-ing. The consumers in 1ri-ona use

the grill year round and it is stored on the dec where it is sub+ect to sunlight, rain and

higher temperatures. The manufacturer must mae sure that the grill can withstand

 both conditions. If the free-ing temperature cracs the valve connection or if the heat

cause the lid to deform, they will lose the potential buyers in the respective area.

What can be said in conc!usion?

Robust design is designing a way to mae the final product consistent when the

 process is sub+ect to a variety of noise. This can be done through a variety of 

e$perimentation methods. The results are capable of showing how to develop a

 product=process that will be robust. The advantages of robust design are that the

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 products are of good quality, cheaper, and more customer friendly than their non5

robust counterparts. 1lthough there are disadvantages, having a robust product design

can give companies a large competitive edge.

Why "se Robust Design Method?

@ver the last five years many leading companies have invested heavily in the Ei$

Eigma approach aimed at reducing waste during manufacturing and operations. These

efforts have had great impact on the cost structure and hence on the bottom line of 

those companies. (any of them have reached the ma$imum potential of the

traditional Ei$ Eigma approach. /hat would be the engine for the ne$t wave of 

 productivity improvementF

>renda Reichelderfer of ITT Industries reported on their benchmaring survey of 

many leading companies, design directly influences more than 8H of the product

life cycle costC companies with high product development effectiveness have earningsthree times the average earningsC and companies with high product development

effectiveness have revenue growth two times the average revenue growth. Ehe also

observed, KH of product development costs are wastedL

These and similar observations by other leading companies are compelling them to

adopt improved product development processes under the banner Design for Ei$

Eigma. The Design for #i #igma approach is focused on )" increasing engineering

 productivity so that new products can be developed rapidly and at low cost, and 3"

value based management.

Robust Design method is central to improving engineering productivity.

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research in a short time and come up with a design that would not embarrass the

company again in the fieldF

The Robust Design method has he!ped reduce the deve!opment time and cost by

a factor of two or better in many such prob!ems%

In general, engineering decisions involved in product=system development can beclassified into two categories#

• %rror5free implementation of the past collective nowledge and e$perience

• Generation of new design information, often for improving product

quality=reliability, performance, and cost.

/hile ?1D=?1% tools are effective for implementing past nowledge, Robust Design

method greatly improves productivity in generation of new nowledge by acting as an

amplifier of engineering sills. /ith Robust Design, a company can rapidly achieve

the full technological potential of their design ideas and achieve higher profits.

% Robustness #trategy

Mariation reduction is universally recogni-ed as a ey to reliability and productivity

improvement. There are many approaches to reducing the variability, each one having

its place in the product development cycle.

>y addressing variation reduction at a particular stage in a products life cycle, one

can prevent failures in the downstream stages. The Ei$ Eigma approach has made

tremendous gains in cost reduction by finding problems that occur in manufacturing

or white5collar operations and fi$ing the immediate causes. The robustness strategy is

to prevent problems through optimi-ing product designs and manufacturing process

designs.

The manufacturer of a differential op5amplifier used in coin telephones faced the

 problem of e$cessive offset voltage due to manufacturing variability. 0igh offset

voltage caused poor voice quality, especially for phones further away from the central

office. Eo, how to minimi-e field problems and associated costF There are many

approaches#

). ?ompensate the customers for their losses.

3. Ecreen out circuits having large offset voltage at the end of the production line.A. Institute tighter tolerances through process control on the manufacturing line.

K. ?hange the nominal values of critical circuit parameters such that the circuits

function becomes insensitive to the cause, namely, manufacturing variation.

The approach K is the robustness strategy. 1s one moves from approach ) to K, one

 progressively moves upstream in the product delivery cycle and also becomes more

efficient in cost control. 0ence it is preferable to address the problem as upstream as

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 possible. The robustness strategy provides the crucial methodology for systematically

arriving at solutions that mae designs less sensitive to various causes of variation. It

can be used for optimi-ing product design as well as for manufacturing process

design.

The Robustness Etrategy uses five primary tools#

).

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 e$t consider the parameters=factors that are beyond the control of the designer.

Those factors are called noise factors. @utside temperature, opening=closing of 

windows, and number of occupants are e$amples of noise factors.

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'et us define the following variables#

m# target value for a critical product characteristic

4=5 DeltaH# allowed deviation from the target

1H# loss due to a defective product

Then the quality loss, ', suffered by an average customer due to a product with y as

value of the characteristic is given by the following equation#

 L = k * ( y – m )2

where 7 ! 1H = DeltaH3

 "If the output of the factory has distribution of the critical characteristic with mean m

and variance s3, then the average quality loss per unit of the product is given by#

Q = k { ( mu – m )2 + sigma2 }

%, #igna! To -oise (#.-) Ratios

The product=process=system design phase involves deciding the best values=levels for 

the control factors. The signal to noise !E=" ratio is an ideal metric for that purpose.

The equation for average quality loss, ;, says that the customers average quality lossdepends on the deviation of the mean from the target and also on the variance. 1n

important class of design optimi-ation problem requires minimi-ation of the variance

while eeping the mean on target.

>etween the mean and standard deviation, it is typically easy to ad+ust the mean on

target, but reducing the variance is difficult. Therefore, the designer should minimi-e

the variance first and then ad+ust the mean on target.1mong the available control

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factors most of them should be used to reduce variance. @nly one or two control

factors are adequate for ad+usting the mean on target.

The design optimi-ation problem can be solved in two steps#

). (a$imi-e the E= ratio, h, defined ash = 10 log 10 ( h

2~ / sigma2 )

This is the step of variance reduction.

3. 1d+ust the mean on target using a control factor that has no effect on h. Euch a

factor is called a scaling factor. This is the step of ad+usting the mean on target.

@ne typically loos for one scaling factor to ad+ust the mean on target during design

and another for ad+usting the mean to compensate for process variation during

manufacturing.

%/ #tatic 0ersus Dynamic #.- Ratios

In some engineering problems, the signal factor is absent or it taes a fi$ed value.

These problems are called Etatic problems and the corresponding E= ratios are called

static E= ratios. The E= ratio described in the preceding section is a static E= ratio.

In other problems, the signal and response must follow a function called the ideal

function. In the cooling system e$ample described earlier, the response !room

temperature" and signal !set point" must follow a linear relationship. Euch problems

are called dynamic problems and the corresponding E= ratios are called dynamic E=

ratios.

The dynamic E= ratio will be illustrated in a later section using a turbine design

e$ample.

Dynamic E= ratios are very useful for technology development, which is the process

of generating fle$ible solutions that can be used in many products.

,% #teps in Robust &arameter Design

Robust

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The e$periments may be conducted in hardware or through simulation. It is not

necessary to have a full5scale model of the product for the purpose of 

e$perimentation. It is sufficient and more desirable to have an essential model of the

 product that adequately captures the design concept. Thus, the e$periments can be

done more economically.

,% 1actor 4ffects 'na!ysis2

The effects of the control factors are calculated in this step and the results are

analy-ed to select optimum setting of the control factors.

/% &rediction.3onfirmation2

In order to validate the optimum conditions we predict the performance of the product

design under baseline and optimum settings of the control factors. Then we perform

confirmation e$periments under these conditions and compare the results with the

 predictions. If the results of confirmation e$periments agree with the predictions, then

we implement the results. @therwise, the above steps must be iterated.