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Introduction to the Taguchi Method
2018We now introduce a further powerful engineering tool: the Robustness concept. It is inextricably linked to the name of Genichi Taguchi, a Japanese engineer. Although his approach is based on what was outlined in the previous chapter on Design of Experiment, it contains strong innovation of those concepts. Significant improvements are proposed for three
Alessandro Freddi, Mario Salmon
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Taguchi Method Using Intelligent Techniques
2015The Taguchi method has been widely applied in quality management applications to identify and fix key factors contributing to the variations of product quality in manufacturing processes. This method combines engineering and statistical methods to achieve improvements in cost and quality by optimizing product designs and manufacturing processes.
Kok-Zuea Tang +2 more
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Taguchi methods and sample surveys
Total Quality Management, 1994Methods of quality improvement should be applied to the service industry as well as to the manufacturing industry. Sample surveys are used to illustrate both how these methods might be applied and some of the conceptual difficulties that could be encountered.
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Taguchi Methods: A Hands-On Approach
Journal of Quality Technology, 1994(1994). Taguchi Methods: A Hands-On Approach. Journal of Quality Technology: Vol. 26, No. 1, pp. 77-78.
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Taguchi Methods® and Robust Design
2016Dr. Genichi Taguchi (Taguchi and Wu 1980) described methods for using designed experiments in designing and improving products and processes. The Taguchi approach differs from the methods described in previous chapters, mostly due to their lack of fitting polynomial models to data.
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Taguchi methods in software quality testing
International Journal of Quality Engineering and Technology, 2010Quality optimisation methods offer a structured way of improving software development as early as in the product design phase. This work introduces the Taguchi method in software design optimisation. The basics of the method are reviewed along with a proposed methodology for design of experiments (DOE).
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