Optimization of Materials Selection in a Multi-Part Environment with Direct Multi-Search

Abstract

Materials selection is a matter of great importance to engineering design and software tools are valuable to inform decisions in product development. When a pool of alternative materials is available for different parts, the question of what optimal material mix to select for a set of parts is not simple. In engineering problems, the designer decides about the part shape and material and their decision largely determines the part cost and weight. However, cost and weight are only possible to calculate when the part geometrical attributes and production process elements are defined. As a result of the large number of permutations, exhaustive search is not possible to justify the use of an optimization procedure to determine the optimal solution. Another aspect of the optimization procedure is that it needs to deal with nondifferentiable objective functions and constraints. To solve this multi-objective optimization (MOO) problem, a new routine based on the direct multisearch (DMS) algorithm [1] is proposed. An example from industry has been solved using this new methodology based on DMS. Results from the Pareto front can help the designer to align the material selection for a complete set of materials with product attribute objectives, depending on the relative importance of each objective. The results illustrate the capacity of this DMS model to solve the optimization problem with reasonable computational time.