TAUCHI PHILOSOPHY SUBMITTED BY: RAKESH KUMAR ME103111

History & Background High quality product or service and the associated customer satisfaction is key for enterprises survival In traditional method of quality improvement during pre-production experiment only one factor is adjusted. This method is costly and unreliable R.Fisher developed method using statistical design of experiments(SDE) Taguchi uses off-line quality control to make a design robust against variability in both production and user environment

Taguchi philosophy In a competitive market environment, continual quality improvements and cost reductions are necessary for business survival. An important measurement of the quality of a manufactured product is the total loss generated by that product to the society. Change the pre-production experimental procedure from varying one factor at a time to varying many factors simultaneously (SDE), so that quality can be built into the product and the process.

Continue…. The customer's loss due to poor quality is approximately proportional to the square of the deviation of the performance characteristic from its target or nominal value. A product (or service) performance variation can be reduced by examining the non-linear effects of factors (parameters) on the performance characteristics. Any deviation from a target leads to poor quality.

Taguchi methods Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured goods, and also applied to, engineering, biotechnology, marketing & advertising. Taguchi work includes 3 principal contribution to statistics:  Taguchi loss function  The philosophy of offline control  Innovations in the design of experiments

Taguchi’s definition of quality "quality is the loss a product causes to society after being shipped, other than losses caused by its intrinsic functions.“ two categories: (1) loss caused by variability of function (2) loss caused by harmful side effects.

Continue…. 4 quality concepts devised by Taguchi:- Quality should be designed into the product from the start, not by inspection and screening. Quality is best achieved by minimising the deviation from the target, not a failure to confirm to specifications. Quality should not be based on the performance, features or characteristics of the product. The cost of quality should be measured as a function of product performance variation and the losses measured system- wide.

TAGUCHI'S LOSS FUNCTION For every product quality characteristic there is a target value which results in the smallest loss; deviations from target value always results in increased loss to society small deviations from the target value result in small losses. These losses, however, increase in a nonlinear fashion as deviations from the target value increase.

Continue……… Taguchi specified three situations: Larger the better (for example, agricultural yield); Smaller the better (for example, carbon dioxide emissions); andcarbon dioxide On-target, minimum-variation (for example, a mating part in an assembly).

Mathematical model of loss function The parabolic curve of the Taguchi loss function can be represented as below: Loss at any point L(x) = C*(x-t) ^2…………….Eqn. 1.1 Where, C = Coefficient of Taguchi loss x = A point on the curve t = Target Value (Nominal value) Average Taguchi loss per item for a sample set: L= C*(d^2+ (m –t) ^2)………………Eqn.1.2 Where, d = standard deviation m = Process mean t = Target Value (Nominal value)

OFF-LINE QUALITY CONTROL Taguchi's rule for manufacturing the best opportunity to eliminate variation is during the design of a product and its manufacturing process The process has three stages: System design Parameter design Tolerance design

Design of experiments Taguchi's framework for design of experiments is idiosyncratic and often flawed, but contains much that is of enormous value. He made a number of innovations.design of experimentsidiosyncratic Orthogonal arrays Taguchi employs design experiments using specially constructed table, known as "Orthogonal Arrays (OA)" to treat the design process, such that the quality is build into the product during the product design stage.

Orthogonal array of L8

STEPS IN TAGUCHI METHODOLOGY: Step-1: IDENTIFY THE MAIN FUNCTION, SIDE EFFECTS, AND FAILURE MODE Step-2: IDENTIFY THE NOISE FACTORS, TESTING CONDITIONS, AND QUALITY CHARACTERISTICS Step-3: IDENTIFY THE OBJECTIVE FUNCTION TO BE OPTIMIZED Step-4: IDENTIFY THE CONTROL FACTORS AND THEIR LEVELS Step-5: SELECT THE ORTHOGONAL ARRAY MATRIX EXPERIMENT Step-6: CONDUCT THE MATRIX EXPERIMENT Step-7: ANALYZE THE DATA, PREDICT THE OPTIMUM LEVELS AND PERFORMANCE Step-8: PERFORM THE VERIFICATION EXPERIMENT AND PLAN THE FUTURE ACTION

Array selector

Analyzing experimental data Once the experimental design has been determined and the trials have been carried out, the measured performance characteristic from each trial can be used to analyze the relative effect of the different parameters To determine the effect each variable has on the output, the signal-to-noise ratio, or the SN number, needs to be calculated for each experiment conducted.

Smaller the better SNs= -10log(∑Yi²/n) Here I =1 to n Larger the better SNs=-10log(∑(1/Yi²)(1/n))

After calculating the SN ratio for each experiment, the average SN value is calculated for each factor and level. Once these SN ratio values are calculated for each factor and level, they are tabulated as shown below and the range R (R = high SN - low SN)of the SN for each parameter is calculated and entered into the table. The larger the R value for a parameter, the larger the effect the variable has on the process. This is because the same change in signal causes a larger effect on the output variable being measured.

Advantages It emphasizes a mean performance characteristic value close to the target value rather than a value within certain specification limits, thus improving the product quality. Taguchi's method for experimental design is straightforward and easy to apply to many engineering situations, making it a powerful yet simple tool. It can be used to quickly narrow down the scope of a research project or to identify problems in a manufacturing process from data already in existence

Continue…. Taguchi method allows for the analysis of many different parameters without a prohibitively high amount of experimentation it allows for the identification of key parameters that have the most effect on the performance characteristic value so that further experimentation on these parameters can be performed and the parameters that have little effect can be ignored

Disadvantages results obtained are only relative and do not exactly indicate what parameter has the highest effect on the performance characteristic value Also, since orthogonal arrays do not test all variable combinations, this method should not be used with all relationships between all variables are needed. The Taguchi method has been criticized in the literature for difficulty in accounting for interactions between parameters.

continue Taguchi methods are offline, and therefore inappropriate for a dynamically changing process such as a simulation study. since Taguchi methods deal with designing quality in rather than correcting for poor quality, they are applied most effectively at early stages of process development. After design variables are specified, use of experimental design may be less cost effective.