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Multi-objective optimization of build orientation considering support structure volume and build time in laser powder bed fusion

  • Ahmet Can Günaydın

    Ahmet Can Günaydın received both B.Sc. and M.Sc. degrees in mechanical engineering from Selçuk University, Konya, Turkey, in 2014 and 2017, respectively. He is currently pursuing the Ph.D. degree in mechanical engineering with Bursa Uludağ University, Bursa, Turkey. He is also a design engineer at TAI Uludağ University R&D Center. His current research interests include additive manufacturing and multi-objective optimization techniques.

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    , Ali Rıza Yıldız ORCID logo and Necmettin Kaya ORCID logo
Published/Copyright: March 16, 2022
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Abstract

Additive manufacturing is a production technology based on creating three-dimensional parts directly from computer-aided design data layer-by-layer. In recent years, it has been used in many industries with the production of functional, high-quality metallic parts with the powder bed fusion process by laser. The build orientation of the three-dimensional part has a major impact on many factors such as part quality, waste amount, production time, and cost. In this study, a multi-objective optimization is carried out using non-dominated sorting genetic algorithm-II to simultaneously optimize different objectives that may conflict with each other, such as the amount of support structure and build time. Estimation methods are developed for computing the amount of support structure and the build time, which reflect the current state of the technology. With the developed method, build orientation is optimized for a complex part, and the wide range of alternative results are visualized and evaluated. The design for additive manufacturing knowledge required to correctly perform the build orientation process is eliminated by automating the pre-processing stage. Therefore, the contribution is made to the accessibility and sustainability of the PBF-L, which has high process costs by minimizing support structure volume and build time.


Corresponding author: Ahmet Can Günaydın, Turkish Aerospace Industries Inc, 16285, Bursa, Turkey, E-mail:

Award Identifier / Grant number: 118C100

About the author

Ahmet Can Günaydın

Ahmet Can Günaydın received both B.Sc. and M.Sc. degrees in mechanical engineering from Selçuk University, Konya, Turkey, in 2014 and 2017, respectively. He is currently pursuing the Ph.D. degree in mechanical engineering with Bursa Uludağ University, Bursa, Turkey. He is also a design engineer at TAI Uludağ University R&D Center. His current research interests include additive manufacturing and multi-objective optimization techniques.

Acknowledgment

The corresponding author thanks the Scientific and Technological Research Council of Turkey (TÜBİTAK) for their support under 2244 – Industrial PhD Fellowship Program, Grant No: 118C100.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was funded by the Scientific and Technological Research Council of Turkey (TÜBİTAK) (118C100).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] B. S. Yıldız, V. Patel, N. Pholdee, S. M. Sait, S. Bureerat, and A. R. Yıldız, “Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design,” Mater. Test., vol. 63, no. 4, pp. 336–340, 2021, https://doi.org/10.1515/mt-2020-0049.Search in Google Scholar

[2] N. Panagant, M. Yıldız, N. Pholdee, A. R. Yıldız, S. Bureerat, and S. M. Sait, “A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems,” Mater. Test., vol. 63, no. 5, pp. 453–457, 2021, https://doi.org/10.1515/mt-2020-0077.Search in Google Scholar

[3] M. Yıldız, N. Panagant, N. Pholdee, S. Bureerat, S. M. Sait, and A. Rıza Yıldız, “Hybrid Taguchi-Lévy flight distribution optimization algorithm for solving real-world design optimization problems,” Mater. Test., vol. 63, no. 6, pp. 547–551, 2021, https://doi.org/10.1515/mt-2020-0091.Search in Google Scholar

[4] G. Karadere, Y. Düzcan, and A. Rıza Yıldız, “Light-weight design of automobile suspension components using topology and shape optimization techniques,” Mater. Test., vol. 62, no. 5, pp. 454–464, 2020, https://doi.org/10.3139/120.111503.Search in Google Scholar

[5] B. S. Yıldız, A. R. Yıldız, N. Pholdee, S. Bureerat, S. M. Sait, and V. Patel, “The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components,” Mater. Test., vol. 62, no. 3, pp. 261–264, 2020, https://doi.org/10.3139/120.111479.Search in Google Scholar

[6] E. Todorov, R. Spencer, S. Gleeson, M. Jamshidinia, and S. M. Kelly, “America makes: National Additive Manufacturing Innovation Institute (NAMII) project 1: nondestructive evaluation (NDE) of complex metallic additive manufactured (AM) structures, EWI,” Interim, Columbus, Ohio, USA, Report No. AFRL-RX-WP-TR-2014-0162, Jun. 2014.10.21236/ADA612775Search in Google Scholar

[7] B. Aslan and A. R. Yıldız, “Optimum design of automobile components using lattice structures for additive manufacturing,” Mater. Test., vol. 62, no. 6, pp. 633–639, 2020, https://doi.org/10.3139/120.111527.Search in Google Scholar

[8] J. Jiang, X. Xu, and J. Stringer, “Support structures for additive manufacturing: a review,” J. Manuf. Mater. Process., vol. 2, no. 4, p. 64, 2018, https://doi.org/10.3390/jmmp2040064.Search in Google Scholar

[9] E. Malekipour, A. Tovar, and H. El-Mounayri, “Heat conduction and geometry topology optimization of support structure in laser-based additive manufacturing,” in Conference Proceedings of the Society for Experimental Mechanics Series, vol. 9, Cham, Springer, 2018, pp. 17–27.10.1007/978-3-319-62834-9_4Search in Google Scholar

[10] W. Oropallo and L. A. Piegl, “Ten challenges in 3D printing,” Eng. Comput., vol. 32, no. 1, pp. 135–148, 2016, https://doi.org/10.1007/s00366-015-0407-0.Search in Google Scholar

[11] D. Ahn, H. Kim, and S. Lee, “Fabrication direction optimization to minimize post-machining in layered manufacturing,” Int. J. Mach. Tool Manufact., vol. 47, nos. 3–4, pp. 593–606, 2007, https://doi.org/10.1016/j.ijmachtools.2006.05.004.Search in Google Scholar

[12] M. Cloots, A. Spierings, and B. K. Wegener, “Assessing new support minimizing strategies for the additive manufacturing technology SLM,” in Proc. of the 24th International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conf., Austin, TX, USA, The University of Texas at Austin, 24, 2013, pp. 631–643.Search in Google Scholar

[13] D. Gürses, S. Bureerat, S. M. Sait, and A. R. Yıldız, “Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications,” Mater. Test., vol. 63, no. 5, pp. 448–452, 2021, https://doi.org/10.1515/mt-2020-0076.Search in Google Scholar

[14] M. Taufik and P. K. Jain, “Role of build orientation in layered manufacturing: a review,” Int. J. Manuf. Technol. Manag., vol. 27, nos. 1–3, pp. 47–73, 2013, https://doi.org/10.1504/IJMTM.2013.058637.Search in Google Scholar

[15] Y. Zhang and S. K. Moon, “Data-driven design strategy in fused filament fabrication: status and opportunities,” J. Comput. Des. Eng., vol. 8, no. 2, pp. 489–509, 2021, https://doi.org/10.1093/jcde/qwaa094.Search in Google Scholar

[16] R. Stolt and F. Elgh, “Introducing design for selective laser melting in aerospace industry,” J. Comput. Des. Eng., vol. 7, no. 4, pp. 489–497, 2020, https://doi.org/10.1093/jcde/qwaa042.Search in Google Scholar

[17] D. R. Eyers and A. T. Potter, “Industrial additive manufacturing: a manufacturing systems perspective,” Comput. Ind., vols 92–93, pp. 208–218, 2017, https://doi.org/10.1016/j.compind.2017.08.002.Search in Google Scholar

[18] P. M. Pandey, N. Venkata Reddy, and S. G. Dhande, “Part deposition orientation studies in layered manufacturing,” J. Mater. Process. Technol., vol. 185, nos. 1–3, pp. 125–131, 2007, https://doi.org/10.1016/j.jmatprotec.2006.03.120.Search in Google Scholar

[19] Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, “Automatic determination of part build orientation for laser powder bed fusion,” Virtual Phys. Prototyp., vol. 16, no. 1, pp. 29–49, 2020, https://doi.org/10.1080/17452759.2020.1832793.Search in Google Scholar

[20] J. R. Wodziak, G. M. Fadel, and C. Kirschman, “A genetic algorithm for optimizing multiple part placement to reduce build time,” in Proc. of the Fifth International Conf. on Rapid Prototyping, Dayton, OH, USA, University of Dayton, 1994, pp. 201–210.Search in Google Scholar

[21] P. Alexander, S. Allen, and D. Dutta, “Part orientation and build cost determination in layered manufacturing,” CAD Comput. Aided Des., vol. 30, no. 5, pp. 343–356, 1998, https://doi.org/10.1016/S0010-4485(97)00083-3.Search in Google Scholar

[22] H. S. Byun and K. H. Lee, “Determination of optimal build direction in rapid prototyping with variable slicing,” Int. J. Adv. Manuf. Technol., vol. 28, nos. 3–4, pp. 307–313, 2006, https://doi.org/10.1007/s00170-004-2355-5.Search in Google Scholar

[23] P. K. Gurrala and S. P. Regalla, “Multi-objective optimisation of strength and volumetric shrinkage of FDM parts,” Virtual Phys. Prototyp., vol. 9, no. 2, pp. 127–138, 2014, https://doi.org/10.1080/17452759.2014.898851.Search in Google Scholar

[24] E. Ulu, E. Korkmaz, K. Yay, O. B. Ozdoganlar, and L. B. Kara, “Enhancing the structural performance of additively manufactured objects through build orientation optimization,” J. Mech. Des., vol. 137, no. 11, pp. 111410–111418, 2015, https://doi.org/10.1115/1.4030998.Search in Google Scholar

[25] W. M. Wang, C. Zanni, and L. Kobbelt, “Improved surface quality in 3D printing by optimizing the printing direction,” Comput. Graph. Forum, vol. 35, no. 2, pp. 59–70, 2016, https://doi.org/10.1111/cgf.12811.Search in Google Scholar

[26] P. Jaiswal, J. Patel, and R. Rai, “Build orientation optimization for additive manufacturing of functionally graded material objects,” Int. J. Adv. Manuf. Technol., vol. 96, nos. 1–4, pp. 223–235, 2018, https://doi.org/10.1007/s00170-018-1586-9.Search in Google Scholar

[27] L. Di Angelo, P. Di Stefano, A. Dolatnezhadsomarin, E. Guardiani, and E. Khorram, “A reliable build orientation optimization method in additive manufacturing: the application to FDM technology,” Int. J. Adv. Manuf. Technol., vol. 108, nos. 1–2, pp. 263–276, 2020, https://doi.org/10.1007/s00170-020-05359-x.Search in Google Scholar

[28] M. Mele and G. Campana, “Sustainability-driven multi-objective evolutionary orienting in additive manufacturing,” Sustain. Prod. Consum., vol. 23, pp. 138–147, 2020, https://doi.org/10.1016/j.spc.2020.05.004.Search in Google Scholar

[29] A. M. Phatak and S. S. Pande, “Optimum part orientation in rapid prototyping using genetic algorithm,” J. Manuf. Syst., vol. 31, no. 4, pp. 395–402, 2012, https://doi.org/10.1016/j.jmsy.2012.07.001.Search in Google Scholar

[30] S. Pereira, A. I. F. Vaz, and L. N. Vicente, “On the optimal object orientation in additive manufacturing,” Int. J. Adv. Manuf. Technol., vol. 98, nos. 5–8, pp. 1685–1694, 2018, https://doi.org/10.1007/s00170-018-2218-0.Search in Google Scholar

[31] A. M. A. C. Rocha, A. I. Pereira, and A. I. F. Vaz, “Build orientation optimization problem in additive manufacturing,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Cham, Springer International Publishing, 2018, pp. 669–682.10.1007/978-3-319-95165-2_47Search in Google Scholar

[32] M. A. Matos, A. M. A. C. Rocha, and A. I. Pereira, “Improving additive manufacturing performance by build orientation optimization,” Int. J. Adv. Manuf. Technol., vol. 107, nos. 5–6, pp. 1993–2005, 2020, https://doi.org/10.1007/s00170-020-04942-6.Search in Google Scholar

[33] M. A. Matos, A. M. A. C. Rocha, and L. A. Costa, “Many-objective optimization of build part orientation in additive manufacturing,” Int. J. Adv. Manuf. Technol., vol. 112, nos. 3–4, pp. 747–762, 2021, https://doi.org/10.1007/s00170-020-06369-5.Search in Google Scholar

[34] A. Li, Z. Zhang, D. Wang, and J. Yang, “Optimization method to fabrication orientation of parts in fused deposition modeling rapid prototyping,” in 2010 International Conf. on Mechanic Automation and Control Engineering, MACE2010, Wuhan, China, IEEE, 2010, pp. 416–419.10.1109/MACE.2010.5535335Search in Google Scholar

[35] B. Ga, N. Gardan, and G. Wahu, “Methodology for part building orientation in additive manufacturing,” Comput. Aided Des. Appl., vol. 16, no. 1, pp. 113–128, 2018, https://doi.org/10.14733/cadaps.2019.113-128.Search in Google Scholar

[36] A. H. Golmohammadi and S. Khodaygan, “A framework for multi-objective optimisation of 3D part-build orientation with a desired angular resolution in additive manufacturing processes,” Virtual Phys. Prototyp., vol. 14, no. 1, pp. 19–36, 2019, https://doi.org/10.1080/17452759.2018.1526622.Search in Google Scholar

[37] Y. Zhang and A. Bernard, “Using AM feature and multi-attribute decision making to orientate part in additive manufacturing,” in High Value Manufacturing: Advanced Research in Virtual and Rapid Prototyping, Leiria, Portugal, CRC Press, 2013, pp. 411–416.10.1201/b15961-76Search in Google Scholar

[38] K. Thrimurthulu, P. M. Pandey, and N. V. Reddy, “Optimum part deposition orientation in fused deposition modeling,” Int. J. Mach. Tool Manufact., vol. 44, no. 6, pp. 585–594, 2004, https://doi.org/10.1016/j.ijmachtools.2003.12.004.Search in Google Scholar

[39] V. Canellidis, J. Giannatsis, and V. Dedoussis, “Genetic-algorithm-based multi-objective optimization of the build orientation in stereolithography,” Int. J. Adv. Manuf. Technol., vol. 45, nos. 7–8, pp. 714–730, 2009, https://doi.org/10.1007/s00170-009-2006-y.Search in Google Scholar

[40] R. Huang, N. Dai, D. Li, X. Cheng, H. Liu, and D. Sun, “Parallel non-dominated sorting genetic algorithm-II for optimal part deposition orientation in additive manufacturing based on functional features,” Proc. IME C J. Mech. Eng. Sci., vol. 232, no. 19, pp. 3384–3395, 2018, https://doi.org/10.1177/0954406217737105.Search in Google Scholar

[41] Y. Zhang, A. Bernard, R. K. Gupta, and R. Harik, “Feature based building orientation optimization for additive manufacturing,” Rapid Prototyp. J., vol. 22, no. 2, pp. 358–376, 2016, https://doi.org/10.1108/RPJ-03-2014-0037.Search in Google Scholar

[42] Y. Zhang, W. De Backer, R. Harik, and A. Bernard, “Build orientation determination for multi-material deposition additive manufacturing with continuous fibers,” Procedia CIRP, vol. 50, pp. 414–419, 2016, https://doi.org/10.1016/j.procir.2016.04.119.Search in Google Scholar

[43] H. Abderazek, A. Riza Yildiz, and S. M. Sait, “Optimization of constrained mechanical design problems using the equilibrium optimization algorithm,” Mater. Test., vol. 63, no. 6, pp. 552–559, 2021, https://doi.org/10.1515/mt-2020-0092.Search in Google Scholar

[44] H. Abderazek, F. Hamza, A. R. Yildiz, and S. M. Sait, “Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design,” Mater. Test., vol. 63, no. 3, pp. 266–271, 2021, https://doi.org/10.1515/mt-2020-0039.Search in Google Scholar

[45] A. R. Yıldız and M. U. Erdaş, “A new hybrid Taguchi-salp swarm optimization algorithm for the robust design of real-world engineering problems,” Mater. Test., vol. 63, no. 2, pp. 157–162, 2021, https://doi.org/10.1515/mt-2020-0022.Search in Google Scholar

[46] B. S. Yıldız, N. Pholdee, S. Bureerat, A. R. Yıldız, and S. M. Sait, “Sine-cosine optimization algorithm for the conceptual design of automobile components,” Mater. Test., vol. 62, no. 7, pp. 744–748, 2020, https://doi.org/10.3139/120.111541.Search in Google Scholar

[47] B. S. Yıldız, A. R. Yıldız, E. İ. Albak, H. Abderazek, S. M. Sait, and S. Bureerat, “Butterfly optimization algorithm for optimum shape design of automobile suspension components,” Mater. Test., vol. 62, no. 4, pp. 365–370, 2020, https://doi.org/10.3139/120.111492.Search in Google Scholar

[48] E. Kurtuluş, A. R. Yıldız, S. M. Sait, and S. Bureerat, “A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails,” Mater. Test., vol. 62, no. 3, pp. 251–260, 2020, https://doi.org/10.3139/120.111478.Search in Google Scholar

[49] A. R. Yıldız, B. S. Yıldız, S. M. Sait, and X. Li, “The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations,” Mater. Test., vol. 61, no. 8, pp. 725–733, 2019, https://doi.org/10.3139/120.111377.Search in Google Scholar

[50] D. Gürses, N. Pholdee, S. Bureerat, S. M. Sait, and A. R. Yıldız, “A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems,” Mater. Test., vol. 63, no. 6, pp. 560–564, 2021, https://doi.org/10.1515/mt-2020-0093.Search in Google Scholar

[51] A. R. Yıldız, H. Özkaya, M. Yıldız, S. Bureerat, B. S. Yıldız, and S. M. Sait, “The equilibrium optimization algorithm and the response surface-based metamodel for optimal structural design of vehicle components,” Mater. Test., vol. 62, no. 5, pp. 492–496, 2020, https://doi.org/10.3139/120.111509.Search in Google Scholar

[52] A. Balkan, A. R. Yıldız, S. M. Sait, and S. Bureerat, “Optimum design of an air suspension seat using recent structural optimization techniques,” Mater. Test., vol. 62, no. 3, pp. 242–250, 2020, https://doi.org/10.3139/120.111477.Search in Google Scholar

[53] E. Demirci and A. R. Yıldız, “An investigation of the crash performance of magnesium, aluminum and advanced high strength steels and different cross-sections for vehicle thin-walled energy absorbers,” Mater. Test., vol. 60, nos. 7–8, pp. 661–668, 2018, https://doi.org/10.3139/120.111201.Search in Google Scholar

[54] P. Das, R. Chandran, R. Samant, and S. Anand, “Optimum part build orientation in additive manufacturing for minimizing part errors and support structures,” Procedia Manuf., vol. 1, pp. 343–354, 2015, https://doi.org/10.1016/j.promfg.2015.09.041.Search in Google Scholar

[55] H. D. Morgan, J. A. Cherry, S. Jonnalagadda, D. Ewing, and J. Sienz, “Part orientation optimisation for the additive layer manufacture of metal components,” Int. J. Adv. Manuf. Technol., vol. 86, nos. 5–8, pp. 1679–1687, 2016, https://doi.org/10.1007/s00170-015-8151-6.Search in Google Scholar

[56] S. Brika, Y. F. Zhao, M. Brochu, and J. Mezzetta, “Multi-objective build orientation optimization for powder bed fusion by laser,” Ind. Eng. Manag., vol. 6, no. 4, 2017, Art no. 1000236, https://doi.org/10.4172/2169-0316.1000236.Search in Google Scholar

[57] L. Cheng and A. To, “Part-scale build orientation optimization for minimizing residual stress and support volume for metal additive manufacturing: theory and experimental validation,” CAD Comput. Aided Des., vol. 113, pp. 1–23, 2019, https://doi.org/10.1016/j.cad.2019.03.004.Search in Google Scholar

[58] V. Griffiths, J. P. Scanlan, M. H. Eres, A. Martinez-Sykora, and P. Chinchapatnam, “Cost-driven build orientation and bin packing of parts in selective laser melting (SLM),” Eur. J. Oper. Res., vol. 273, no. 1, pp. 334–352, 2019, https://doi.org/10.1016/j.ejor.2018.07.053.Search in Google Scholar

[59] Z. Nie, S. Jung, L. B. Kara, and K. S. Whitefoot, “Optimization of part consolidation for minimum production costs and time using additive manufacturing,” Trans. ASME J. Mech. Des., vol. 142, no. 7, pp. 1–16, 2020, https://doi.org/10.1115/1.4045106.Search in Google Scholar

[60] N. K. Sahu and A. B. Andhare, “Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms,” J. Comput. Des. Eng., vol. 6, no. 1, pp. 1–12, 2019, https://doi.org/10.1016/j.jcde.2018.04.004.Search in Google Scholar

[61] G. Allaire, M. Bihr, and B. Bogosel, “Support optimization in additive manufacturing for geometric and thermo-mechanical constraints,” Struct. Multidiscip. Optim., vol. 61, no. 6, pp. 2377–2399, 2020, https://doi.org/10.1007/s00158-020-02551-1.Search in Google Scholar

[62] M. Szilvási-Nagy and G. Mátyási, “Analysis of STL files,” Math. Comput. Model., vol. 38, nos. 7–9, pp. 945–960, 2003, https://doi.org/10.1016/s0895-7177(03)90079-3.Search in Google Scholar

[63] T. Möller and B. Trumbore, “Fast, minimum storage ray/triangle intersection,” in ACM SIGGRAPH 2005 Courses on – SIGGRAPH ’05, Los Angeles, California, Association for Computing Machinery, 2005, p. 7.10.1145/1198555.1198746Search in Google Scholar

[64] J. Jiang and Y. Ma, “Path planning strategies to optimize accuracy, quality, build time and material use in additive manufacturing: a review,” Micromachines, vol. 11, no. 7, p. 633, 2020, https://doi.org/10.3390/mi11070633.Search in Google Scholar PubMed PubMed Central

[65] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002, https://doi.org/10.1109/4235.996017.Search in Google Scholar

[66] Optimize Manufacturability with Altair Inspire Print3D. Altair, 2021 [Online]. Available at: https://www.altair.com/resource/optimize-manufacturability-with-altair-inspire-print3d [accessed: Sep. 01, 2021].Search in Google Scholar

Published Online: 2022-03-16
Published in Print: 2022-03-28

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