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- W2519800467 abstract "The objective of this study is to propose a systematically efficient approach for automatically arranging factors into an orthogonal array (OA) of design-of-experiment-based Taguchi method for obtaining the best experimental resolution. While constructing the OA, the difficulties in both experimental layouts and the highest resolution design have to take into account. This study developed an effective fitness function to determine the resolution level and applied a genetic algorithm (GA) to search the column numbers and assign specific main factors and factor interactions to an OA for obtaining the highest experimental resolution. The OAs L_(16)(2^(15)) and L_(27)(3^(13)) frequently used in matrix experiments as examples were studied and discussed. From the computational experiments, it can be observed that the GA with the proposed effective fitness function can successfully find the feasible solutions for the best resolution in L_(16)(2^(15)) and L_(27)(3^(13)), and automatically assign main factors and factor interactions to matrix experiments. The best advantage of the systematic approach is to be applied to any specific numbers in terms of main factors and two-factor interactions correspondin to any saturated OAs to automatically assign main factors and factor interactions to matrix experiments for obtaining the best experimental resolution." @default.
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- W2519800467 date "2015-12-01" @default.
- W2519800467 modified "2023-09-23" @default.
- W2519800467 title "Genetic Algorithm for Optimal Resolution Planning in DOE-based Taguchi Method" @default.
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