Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

Luciano Ferreira Cruz, Flavia Bernardo Pinto, Lucas Camilotti, Angelo Marcio Oliveira Santanna (), Roberto Zanetti Freire and Leandro Santos Coelho
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Luciano Ferreira Cruz: Juscelino Kubitschek de Oliveira, 2600
Flavia Bernardo Pinto: Pontifical Catholic University of Parana
Lucas Camilotti: Pontifical Catholic University of Parana
Angelo Marcio Oliveira Santanna: Federal University of Bahia
Roberto Zanetti Freire: Pontifical Catholic University of Parana
Leandro Santos Coelho: Pontifical Catholic University of Parana

Annals of Operations Research, 2022, vol. 319, issue 2, No 5, 1565-1587

Abstract: Abstract Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with non-dominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance. To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms.

Keywords: Multiobjective optimization; Multiobjective differential evolution with spherical pruning; Design of experiment; Machining process (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-021-04232-8

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