Taguchi Technique

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-Sneha Lundia

ITM-14


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Experimental Design Many industrial processes in chemistry and engineering

are not optimized or suffer from seemingly randomvariation. Researchers have found it difficult to makenecessary improvements due to the number of testsrequired.

Experimental design offers a method of getting themaximum information from the minimum number of

tests. In addition, valuable information can be gained oninteractions between variables - interactions are often veryimportant, and may be missed when investigations arecarried out changing one variable at a time.


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Taguchi and Robust Designs The Taguchi method includes a set of tables that enable

main variables and interactions to be investigated in aminimum number of trials.

The Taguchi technique places a great deal of importance onthe reduction in variability of products and processes; inother words to make products and processes more robustand less susceptible to changes due to outside influences

such as raw material variation, temperature, and changesto machines and operators. Improved robustness can oftenbe achieved without major capital expenditure through theuse of these techniques.


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Taguchi Technique The Taguchi approach to quality engineering places a great deal

of emphasis on minimizing variation as the main means ofimproving quality. The idea is to design products and processes

whose performance is not affected by outside conditions and tobuild this in during the development and design stage throughthe use of experimental design. The method includes a set oftables that enable main variables and interactions to beinvestigated in a minimum number of trials.

Though the concept is reasonably straightforward, choosing themost appropriate table and interpreting the results can be more

difficult. There is the danger in using Taguchi of drawing thewrong conclusion. Therefore it is always advisable to check anyresults from a Taguchi matrix; for instance, if the result indicatessome strong effects, it is best to check these in a full matrix orone off runs using one-variable-at-a-time method.


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Robust Design A process that does not change with changing noise is described asbeing robust. For instance, you may have a process making a product

(for example, plastic door handles), but despite your best efforts theproduct quality varies wildly. You would typically concentrate on those

variables you are able to control (raw material quality, machine settingssuch as temperature and time in mould) to try to make a betterproduct. However, the product quality varies due to the variables youare not able to control, such as ambient temperature, humidity, anddifferences due to changes in operators from one shift to another. These

variables are known as noise. Rather than tightening up on the process variables, it is often better to

try to adjust the level of these variables to reduce the effect of noise.This way you end up with a product or process that is not only high in

quality, but gives you consistently high quality. For example, in the case of our plastic door handles, there may be a

combination of the formulation, machine setting, and speed ofmoulding that gives a process that gives consistently good productregardless of the ambient temperature and humidity. To do this youneed to identify which factors have an effect on the variability of the

process. This is done by looking at signal/noise in a robust design.


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General steps involved in the

Taguchi Method1. Define the process objective, or more specifically, a target value

for a performance measure of the process. This may be a flowrate, temperature, etc. The target of a process may also be aminimum or maximum; for example, the goal may be tomaximize the output flow rate. The deviation in theperformance characteristic from the target value is used todefine the loss function for the process.

2. Determine the design parameters affecting the process.Parameters are variables within the process that affect theperformance measure such as temperatures, pressures, etc.that can be easily controlled. The number of levels that theparameters should be varied at must be specified. For example,a temperature might be varied to a low and high value of 40 Cand 80 C. Increasing the number of levels to vary a parameter

at increases the number of experiments to be conducted.


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4. Create orthogonal arrays for the parameter designindicating the number of and conditions for eachexperiment.

5. Conduct the experiments indicated in the completed

array to collect data on the effect on the performancemeasure.

6. Complete data analysis to determine the effect of thedifferent parameters on the performance measure.


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Determining Parameter Design

Orthogonal Array The effect of many different parameters on the

performance characteristic in a condensed set ofexperiments can be examined by using the orthogonalarray experimental design proposed by Taguchi. Oncethe parameters affecting a process that can becontrolled have been determined, the levels at whichthese parameters should be varied must bedetermined.

Knowing the number of parameters and the number oflevels, the proper orthogonal array can be selected.


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L4 Array

L8 Array


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Analyzing Experimental Data Once the experimental design has been determined

and the trials have been carried out, the measuredperformance characteristic from each trial can be usedto analyze the relative effect of the differentparameters.

In this array, it can be seen that any number ofrepeated observations (trials) may be used. Ti,jrepresents the different trials with i = experimentnumber and j = trial number.


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http://controls.engin.umich.edu/wiki/index.php/Image:Orth_an_1.JPG


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After calculating the SN ratio for each experiment, the

average SN value is calculated for each factor and level.This is done as shown below for Parameter 3 (P3) inthe array:


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Once these SN ratio values are calculated for each

factor and level, they are tabulated as shown belowand the range R (R = high SN - low SN)of the SN foreach parameter is calculated and entered into thetable. The larger the R value for a parameter, the larger

the effect the variable has on the process. This isbecause the same change in signal causes a largereffect on the output variable being measured.


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AdvantagesAn advantage of the Taguchi method is that it

emphasizes a mean performance characteristic value

close to the target value rather than a value withincertain specification limits, thus improving theproduct quality.

Additionally, Taguchi's method for experimental

design is straightforward and easy to apply to manyengineering situations, making it a powerful yet simpletool.


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Disadvantages The main disadvantage of the Taguchi method is that

the results obtained are only relative and do notexactly indicate what parameter has the highest effecton the performance characteristic value.

The Taguchi method has been criticized in theliterature for difficulty in accounting for interactionsbetween parameters.


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Use of Taguchi Method in

Procurement Procurement is the acquisition of goods or services. It

is favorable that the goods/services are appropriate

and that they are procured at the best possible cost tomeet the needs of the purchaser in terms of qualityand quantity, time, and location.


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THANK YOU!!

Taguchi Technique

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