Abstract
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.
Highlights
Rubber bumpers built into air spring structures perform several critical tasks, such as working together with the air spring as a secondary spring, modifying the original characteristics of the air spring when pressed together
Using the trained cubic support vector machines (SVM) model, predictions were made for each combination of integer values of design variables
The axisymmetric finite element model for the compression test of the automotive rubber bumper was built with the use of a calibrated two-term Mooney–Rivlin material model
Summary
Rubber bumpers built into air spring structures perform several critical tasks, such as working together with the air spring as a secondary spring, modifying the original characteristics of the air spring when pressed together. In the product design and development cycle, engineers are faced with several predefined requirements that are difficult to fulfill and time consuming, and remain a challenging task. The product investigated is applied in the air springs of lorries, where the force–displacement characteristic for the compression load is one of the most challenging technical requirements. Design engineers manage to achieve the required working characteristics by modifying the shape of the product, which leads to an iterative design process. This process is termed shape optimization whose simplest solution is to determine the optimal geometry through a series of trials with a study called “what if,” based on design engineers’ experiences. If there is an opportunity to parameterize the process from creating a geometry to obtaining the results, conversion that meets the technical requirements can be automated; there might be an opportunity to use optimization algorithms in the design process
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