Optimizing Print Parameters for Maximizing Tensile Strength of Additively Manufactured Polymers Through Evolutionary Algorithms

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  • Additive Manufacturing (AM) has been adopted world- wide for rapid prototyping and small-scale production of complex parts. Despite its widespread adoption, much is still unknown about AM. Many researchers have devoted significant efforts t o studying the importance of AM characteristics such as build time, dimensional accuracy, surface quality, and mechanical behavior in relation to the printing parameters used to create them. Several research have indicated the impacts of printing parameters on the performance of fused filament fabrication (FFF); however, developing a generic model to optimize FFF printing parameters received little attention. This study presents the utilization of genetic programming along with a genetic algorithm to search for the best set of printing parameters that maximizes the tensile strength of PLA samples. The search variables are raster angle, extrusion width, and extrusion temperature. From the test results, genetic programming using Eureqa is conducted to obtain a surrogate model that predicts tensile strength from print parameters. Finally, a genetic algorithm is used to obtain the set of printing parameters that maximizes tensile strength of the test specimen. The proposed model showed a good agreement with experimental data.

    Part of the Proceedings of the Canadian Society for Mechanical Engineering International Congress 2022

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    Attribution-NonCommercial 4.0 International