Automotive Rubber Product Design Using Response Surface Method

Abstract

In rubber bumper design one of the most important technical properties of the product is the force-displacement curve under a compression load, which is a highly nonlinear behavior because of the large deformation, the rubber material and the contacts. Finite element analysis is a good way to evaluate the working characteristics of the rubber part. Fulfillment of customer needs requires a general iterative design method where the objective can be reached with the modification of the product shape. The determination of the optimum requires numerous iterations of finite element analysis which is computationally expensive. With the integration of the Response Surface Method (RSM) into the design process of a two-variable shape optimization task, the optimal design can be achieved more efficiently. Four different Design of Experiments (DOE) methods were used to intelligently chose design points. As a metamodeling technique, Genetic Aggregation was selected to predict the relation between the sampled geometric variables and the nonlinear objective function value. The Nonlinear Programming by Quadratic Lagrangian (NLPQL) and the Mixed-Integer Sequential Quadratic Programming (MISQP) algorithms with different settings were tested to find the optimum of the response surface. As a result, the most accurate and efficient DOE method and optimization algorithm were determined. The introduced Response Surface Method-based optimization is proved to be suitable to determine the shape of the rubber jounce bumper, which meets the technical requirements.