Comparison of Taguchi Method and Robust Design Optimization (RDO) : by application of a functional adaptive simulation model for the robust product-optimization of an adjuster unit

Abstract

Current research and development have been trending towards approaches based on simulation and virtual testing. Industrial development processes for complex products employ optimization methods to ensure results are close to reality, simultaneously minimizing required resources. The results of virtual testing are optimized in accordance with requirements using optimization techniques. Robust Design Optimization (RDO) is one established approach to optimization. RDO is based on the identification of an optimal parameter set which includes a small variance of the target value as a constraint. Under most circumstances, this approach does not involve separate optimization of the target value and target variance. However, the basic strategy of the optimization approach developed by Taguchi is to first optimize the parameter sets for the target value and then optimize and minimize the target variance. According to an application example , the benefit of Taguchi's approach (TM) is that it facilitates the identification of an optimal parameter set of nominal values for technical feasibility and possible manufacturing. If an optimal parameter set is determined, the variance can be minimized under consideration of process parameters. This paper examines and discusses the differences between and shared characteristics of the robust optimization methods TM and RDO, and discusses their shortcomings. In order to provide a better illustration, this paper explains and applies both methods using an adjuster unit of a commercial vehicle braking system. A simulation model is developed including an appropriate work ow by applying optiSLang-modules.