A Study On Robust Design Optimization For Multiple Response Problems

This thesis studies the methods of robust design optimization for multiresponse problems. As an important support tool in Continuous Quality Improvement (CQI), robust design optimization can be used in product design stage to improve product or process quality. Former studies were focused on single response problems. This doesn't satisfy current customers' needs because of the change of market circumstances. It is urgent to find solutions of multiresponse robust optimization.This thesis proposes a process to solve this problem by three stages: choosing a suitable robust design method, determining a right multiresponse optimization tool, and transforming general optimization to robust optimization. Firstly, the author studies two robust design methods: Taguchi Parameter Design and Dual Response Surface Model. This thesis analyses their weaknesses and advantages, and proposes some criteria of choice. Secondly, this thesis studies three traditional multiresponse optimization methods, and brings forward a new method: multiresponse optimization based on evidential reasoning theory. At the same time, weaknesses, advantages and applied scope of these motheds are discussed. At last, the author builds two models: multiresponse robust optimization model based on S/N ratios and multiresponse robust optimization model based on DRSM and desirability function, which ground on the former two theory foundations. Furthermore, comparative research between them is executed.Applied research of a multiresponse experiment in V-process is implemented to reflect the difference between general optimization and robust optimization for multiresponse. From this example, the following conclusion can be drawn: In general, the methods presented in this thesis are more robust than the methods of general multiresponse optimization.