PRODUCT DESIGN OPTIMIZATION BASED ON ROBUSTNESS BY LIMITED EXPERIMENTAL DATA

摘要

As proposed by Taguchi, the objective of robust design is to make a product or process least sensitive to manufacturing variations, deterioration over time and environmental conditions. This is a cost-effective way to improve quality, because it builds quality into products and processes through design, simultaneously with little impact on cost. The experiments of a project were performed by a company three years ago, based on a L_(27) orthogonal array. The design factors were inappropriately assigned into the orthogonal array, including one 2-level factor and twelve 3-level factors. The experiments were expensive and can not be repeated now. In this paper, these data are sorted and utilized to perform robustness analysis, such that we can find significant main effects and significant interactions. Linear graphs developed from the interaction table are used to assign interactions and main effects, to perform multiple analysis of variances (ANOVAs). Through that, the significant effects and interactions can be identified so that the statistical model can be developed for product performance prediction. For confidential reasons, the data are normalized (coded) by a certain mechanism.