Journal of Additive Manufacturing Technologies
Vol 2 No 1 (2022): J AM Tech
https://doi.org/10.18416/JAMTECH.2212704

Articles, ID 704

Combination of peridynamics and genetic algorithm based topology optimization methods for AM-friendly designs

Main Article Content

Abdullah Kendibilir (Sabanci University), Mehmet Furkan Polat (Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey), Omer Safa Cavus (Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey), Sina Khalilvandi Behrouzyar (Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey), Peyman Lahe Motlagh (Department of Mechanical Engineering, Gebze Technical University, Gebze,, Kocaeli, Turkey), Bahattin Koc (Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey), Adnan Kefal (Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey)

Abstract

Topology optimization (TO) is a practical tool to generate light-weighted engineering structures for various manufacturing industries. However, manufacturing constraints and surface smoothing are still considerable challenges for TO algorithms. Existing TO frameworks utilize mechanical analysis approaches that discretize the whole domain with elements or particles. Therefore, obtained geometries from TO have been criticized for their complex shapes. In this study, we propose a coupled framework to generate AM-friendly designs which result in less support structure and higher surface quality. For this purpose, the generative topology optimization method (GTO), which uses genetic algorithm to search for the best alternative set of geometry within all the possible topology results, is coupled with the peridynamics topology optimization (PD-TO) method to evolve the PD-TO results into AM-friendly shapes. The PD-TO discretizes the problem domain using equally spaced particles during the TO process. Hence, PD-TO generates a point cloud file with relevant artificial material density values in the final state. Then, the GTO method utilizes the point cloud and material densities as an input file to achieve better final geometry. AM-friendly designs achieved from GTO are compared with the initial results obtained from PD-TO to demonstrate the efficiency and capability of the proposed method.  

Article Details

How to Cite

Kendibilir, A., Polat, M. F., Cavus, O. S., Behrouzyar, S. K., Motlagh, P. L., Koc, B., & Kefal, A. (2023). Combination of peridynamics and genetic algorithm based topology optimization methods for AM-friendly designs. Journal of Additive Manufacturing Technologies, 2(1), 704. https://doi.org/10.18416/JAMTECH.2212704