Parameter Selection of Local Search Algorithm for Design Optimization of Automotive Rubber Bumper

In rubber bumper design, the most important mechanical property of the product is the force–displacement curve under compression and its fulfillment requires an iterative design method. Design engineers can handle this task with the modification of the product shape, which can be solved with several optimization methods if the parameterization of the design process is determined. The numerical method is a good way to evaluate the working characteristics of the rubber product; furthermore, automation of the whole process is feasible with the use of Visual Basic for Application. An axisymmetric finite element model of a rubber bumper was built with the use of a calibrated two-term Mooney–Rivlin material model. A two-dimensional shape optimization problem was introduced where the objective function was determined as the difference between the initial and the optimum characteristics. Our goal was to integrate a surrogate model-based parameter selection of local search algorithms for the optimization process. As a metamodeling technique, cubic support vector regression was selected and seemed to be suitable to accurately predict the nonlinear objective function. The novel optimization procedure which applied the support vector regression model in the parameter selection process of the stochastic search algorithm proved to be an efficient method to find the global optimum of the investigated problem.