Parameterization and Multiobjective Optimization
CHALLENGE - The main goal of this paper is to show the way in which single and multi-objective optimization can be implemented to improve new products. The author found an optimal strategy for model parameterization of a control arm complying with four criteria and parameterized it performing automatic and manual optimization to verify the criteria to comply with were met. The overall goal is to reduce weight by 10% the control arm while maintaining the stiffness
SOLUTION - After the optimal parameterization strategy and the automatic optimization in the internal optimization module were found five different algorithms were tested in modeFRONTIER with default preferences and were benchmarked against each other. This was done in order to test the efficiency of the algorithms and to find the best two to proceed with a more extensive optimization with a following local search if those two algorithms can generate better results. The best result generated a saving in weight of 3.56% produced by the Hybrid algorithm.
BENEFITS - Thanks to modeFRONTIER it was possible to find an optimal approach for optimizations with no previous information to predict which algorithm performs best. The optimization tool helped in benchmarking five different algorithms and evaluate the result with respect to ease of use and final product requirements. The criteria for ease of use is based on how much time each optimization in modeFRONTIER took.