Taguchi Robust Design

Genichi Taguchi, a Japanese engineer, published his first book on experimental design in 1958. His methods of fractional factorial design have certainly earned him the most well known "brand" in the field of Design of Experiments. The aim of the Taguchi design is to make a product or process more stable in the face of variations over which we have little or no control, for example, making sure that a car engine can perform reliably under different ambient temperatures.

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Taguchi separates variables in two types. Control factors are those variables that can be practically and economically controlled, such as dimensions, material parameters, etc. Noise factors are variables which are difficult or expensive to control in practice, though they can be controlled in an experiment, such as the ambient temperature, the worker of a manufacturing plant, etc. The objective is to determine the combination of control factor settings that will make the product have the maximum robustness to the expected variation in the noise factors. The measure of robustness is the signal-to-noise ratio, a log function of the desired output measurement.

In order to find the best levels of the control factors, the experiments, based on “Orthogonal arrays”, are balanced with respect to all control factors and yet are minimum in number.

Taguchi defines two kinds of categories of problems: static and dynamic. While Dynamic problems have a signal factor (for example: rpm of an engine), Static problems do not have any signal factor. In static problems, the optimization is achieved by using three Signal-to-Noise ratios: smaller is better, larger is better, nominal is best. In dynamic problems, the optimization is achieved by using two signal-to-noise ratios, the Slope and Linearity.

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Optimus enables the Taguchi Method through a comprehensive user interface. Based on orthogonal arrays, the Taguchi DOE provides a reduced variance for the experiments with an optimum setting of the control factors. The resulting robustness and reliability indicators of the engineering problems are enabled through a rich and complete set of Taguchi functions.