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

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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.

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Acknowledgements

The authors thank the financial support from the National Council of Scientific and Technological Development (CNPq), under Grants: 486707/2013-0, 304783/2017-0, and from the Coordination of Improvement of Higher Education Personnel (CAPES). The authors gratefully acknowledge the anonymous reviewers for their valuable comments and suggestions which contributed to improving the paper quality.

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Authors and Affiliations

  1. Volvo of Brazil Vehicles, Juscelino Kubitschek de Oliveira, 2600, Curitiba, 81260-900, Brazil

    Luciano Ferreira Cruz

  2. Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Imaculada Conceição, 1155, Curitiba, 80215-901, Brazil

    Luciano Ferreira Cruz, Flavia Bernardo Pinto, Lucas Camilotti, Roberto Zanetti Freire & Leandro dos Santos Coelho

  3. Industrial Engineering Graduate Program, Federal University of Bahia, prof. Aristides Novis, 2, Salvador, 41250-360, Brazil

    Angelo Marcio Oliveira Santanna

  4. Department of Electrical Engineering, Federal University of Paraná, Cel. Francisco Heráclito dos Santos, 100, Curitiba, 81531-980, Brazil

    Leandro dos Santos Coelho

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  1. Luciano Ferreira Cruz
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Correspondence to Angelo Marcio Oliveira Santanna.

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Cruz, L.F., Pinto, F.B., Camilotti, L. et al. Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process. Ann Oper Res 319, 1565–1587 (2022). https://doi.org/10.1007/s10479-021-04232-8

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