A Monte Carlo Based Robustness Optimization Method in New Product Design Process: A Case Study
ABSTRACT

Monte Carlo method can analyze, solve and optimize many mathematical or physical problems through generating a large number of statistical random samples to simulating stochastic events. It also can be used to remarkably improve design quality of new product. In new product design process, setting distribution characteristics of the design variables is vital to product quality and production robustness. Firstly, response surface model between output characteristics and design variables in new product design is proposed, and the distribution characteristics of design variables and response output are analyzed; then position error model of response output and standard value and allowed error maximum is presented; and then the differences of position error model and allowed error maximum are counted, and reliability ratio is built and calculated, and design robustness of the new product is increased by adjusting the precision value of random design variables in Monte Carlo experiments. Finally, a case is brought forward to verify the validity of the method.

Cite this paper
Che, J. , Wang, J. and Li, K. (2014) A Monte Carlo Based Robustness Optimization Method in New Product Design Process: A Case Study. American Journal of Industrial and Business Management, 4, 360-369. doi: 10.4236/ajibm.2014.47044.
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