Optimizing product design by application of experimental design for computer simulation

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Computer simulation has contributed the recent research and development stage, product design stage and so forth very much in terms of cost reduction, early development. The reason of the popularity is that experiments by computer simulation do not require performing the physical experiments. In this research, we has proposed a general approach for optimization of the product design whose central is design of experiments that is a series of statistical techniques for data collection and analysis. One of the main topics in this research is a technique to validate the developing computer simulation model. The importance of this topic was obtained by discussion with practitioners who work in automotive industry. Through the discussion, the validation practices in automotive industry are obtained, such as cost constraints, time schedule and so forth. Based on the practical requirements, statistical necessity has been formulated. Furthermore, an algorithm for sequential experiments and the … More criteria for design of experiments have been obtained with considering the statistical necessity. In addition, we focus on screening techniques to find important factors from a lot of candidate factors and approximation techniques to describe the relation between responses and factors in order to utilize the developed computer simulator. As regard the screening, we propose an application of supersaturated design and stepwise selection when there are too many factors for the traditional approach by fractional factorial design and statistical tests. While the traditional approach has the limitation of number of factors that can be accommodated in experiments, proposed approach can include more factors than the traditional approach. As regard the approximation, it was found that uniform design is suitable to describe a complicated relation between responses and factors while composite design and second order model is a popular approach. The proposed approach has a flexibility to examine various models to approximate the relation between responses and factors comparing with the traditional approach. Less