Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective decision making

Abstract

Selective laser melting (SLM) is a unique additive manufacturing (AM) category that can be used to manufacture mechanical parts. It has been widely used in aerospace and automotive using metal or alloy powder. The build orientation is crucial in AM because it affects the as-built part, including its part accuracy, surface roughness, support structure, and build time and cost. A mechanical part is usually composed of multiple surface features. The surface features carry the production and design knowledge, which can be utilized in SLM fabrication. This study proposes a method to determine the build orientation of multi-feature mechanical parts (MFMPs) in SLM. First, the surface features of an MFMP are recognized and grouped for formulating the particular optimization objectives. Second, the estimation models of involved optimization objectives are established, and a set of alternative build orientations (ABOs) is further obtained by many-objective optimization. Lastly, a multi-objective decision making method integrated by the technique for order of preference by similarity to the ideal solution and cosine similarity measure is presented to select an optimal build orientation from those ABOs. The weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchy process. Two case studies are reported to validate the proposed method with numerical results, and the effectiveness comparison is presented. Physical manufacturing is conducted to prove the performance of the proposed method. The measured average sampling surface roughness of the most crucial feature of the bracket in the original orientation and the orientations obtained by the weighted sum model and the proposed method are 15.82, 10.84, and 10.62 μm, respectively. The numerical and physical validation results demonstrate that the proposed method is desirable to determine the build orientations of MFMPs with competitive results in SLM.

Graphical abstract

Keywords

selective laser melting (SLM) / build orientation determination / multi-feature mechanical part (MFMP) / fuzzy analytical hierarchy process / multi-objective decision making (MODM)

References

[1] Niu X D, Singh S, Garg A, Singh H, Panda B, Peng X B, Zhang Q J. Review of materials used in laser-aided additive manufacturing processes to produce metallic products. Frontiers of Mechanical Engineering, 2019, 14(3): 282–298

[2] Cao Q Q, Zhang J, Chang S, Fuh J Y H, Wang H. The effect of support structures on maraging steel MS1 parts fabricated by selective laser melting at different building angles. Rapid Prototyping Journal, 2020, 26(9): 1465–1476

[3] Sing S L, Yeong W Y. Laser powder bed fusion for metal additive manufacturing: perspectives on recent developments. Virtual and Physical Prototyping, 2020, 15(3): 359–370

[4] Lee J M, Sing S L, Zhou M M, Yeong W Y. 3D bioprinting processes: a perspective on classification and terminology. International Journal of Bioprinting, 2018, 4(2): 151

[5] Kaynak Y, Kitay O. The effect of post-processing operations on surface characteristics of 316L stainless steel produced by selective laser melting. Additive Manufacturing, 2019, 26: 84–93

[6] Han W, Fang F Z. Orientation effect of electropolishing characteristics of 316L stainless steel fabricated by laser powder bed fusion. Frontiers of Mechanical Engineering, 2021, 16(3): 580–592

[7] Lee J Y, Nagalingam A P, Yeo S H. A review on the state-of-the-art of surface finishing processes and related ISO/ASTM standards for metal additive manufactured components. Virtual and Physical Prototyping, 2021, 16(1): 68–96

[8] Ahsan A N, Habib M A, Khoda B. Resource based process planning for additive manufacturing. Computer-Aided Design, 2015, 69: 112–125

[9] Famodimu O H, Stanford M, Oduoza C F, Zhang L J. Effect of process parameters on the density and porosity of laser melted AlSi10Mg/SiC metal matrix composite. Frontiers of Mechanical Engineering, 2018, 13(4): 520–527

[10] Jiang J C, Xu X, Stringer J. Optimization of process planning for reducing material waste in extrusion based additive manufacturing. Robotics and Computer-Integrated Manufacturing, 2019, 59: 317–325

[11] Zhao D H, Guo W Z. Shape and performance controlled advanced design for additive manufacturing: a review of slicing and path planning. Journal of Manufacturing Science and Engineering, 2020, 142(1): 010801

[12] MorganH D, Cherry J A, JonnalagaddaS, EwingD, SienzJ. Part orientation optimisation for the additive layer manufacture of metal components. The International Journal of Advanced Manufacturing Technology, 2016, 86(5–8): 1679–1687

[13] Brika S E, Zhao Y Y F, Brochu M, Mezzetta J. Multi-objective build orientation optimization for powder bed fusion by laser. Journal of Manufacturing Science and Engineering, 2017, 139(11): 111011

[14] Cheng L, To A. Part-scale build orientation optimization for minimizing residual stress and support volume for metal additive manufacturing: theory and experimental validation. Computer-Aided Design, 2019, 113: 1–23

[15] Griffiths V, Scanlan J P, Eres M H, Martinez-Sykora A, Chinchapatnam P. Cost-driven build orientation and bin packing of parts in selective laser melting (SLM). European Journal of Operational Research, 2019, 273(1): 334–352

[16] Kuo C N, Chua C K, Peng P C, Chen Y W, Sing S L, Huang S, Su Y L. Microstructure evolution and mechanical property response via 3d printing parameter development of Al–Sc alloy. Virtual and Physical Prototyping, 2020, 15(1): 120–129

[17] QinY C, Qi Q F, ShiP Z, ScottP J, JiangX Q. Status, issues, and future of computer-aided part orientation for additive manufacturing. The International Journal of Advanced Manufacturing Technology, 2021, 115(5–6): 1295–1328

[18] MasoodS H, Rattanawong W, IovenittiP. A generic algorithm for a best part orientation system for complex parts in rapid prototyping. Journal of Materials Processing Technology, 2003, 139(1–3): 110–116

[19] Pandey P M, Thrimurthulu K, Reddy N V. Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm. International Journal of Production Research, 2004, 42(19): 4069–4089

[20] Byun H S, Lee K H. Determination of the optimal part orientation in layered manufacturing using a genetic algorithm. International Journal of Production Research, 2005, 43(13): 2709–2724

[21] AhnD, KimH, LeeS. Fabrication direction optimization to minimize post-machining in layered manufacturing. International Journal of Machine Tools and Manufacture, 2007, 47(3–4): 593–606

[22] Byun H S, Lee K H. Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making. Robotics and Computer-Integrated Manufacturing, 2006, 22(1): 69–80

[23] Yu C, Qie L F, Jing S K, Yan Y. Personalized design of part orientation in additive manufacturing. Rapid Prototyping Journal, 2019, 25(10): 1647–1660

[24] Di AngeloL, Di Stefano P, DolatnezhadsomarinA, GuardianiE, Khorram E. A reliable build orientation optimization method in additive manufacturing: the application to FDM technology. The International Journal of Advanced Manufacturing Technology, 2020, 108(1–2): 263–276

[25] Golmohammadi A H, Khodaygan S. A framework for multi-objective optimisation of 3D part-build orientation with a desired angular resolution in additive manufacturing processes. Virtual and Physical Prototyping, 2019, 14(1): 19–36

[26] MatosM A, Rocha A M A C, PereiraA I. Improving additive manufacturing performance by build orientation optimization. The International Journal of Advanced Manufacturing Technology, 2020, 107(5–6): 1993–2005

[27] Matos M A, Rocha A M A C, Costa L A. Many-objective optimization of build part orientation in additive manufacturing. The International Journal of Advanced Manufacturing Technology, 2021, 112(3–4): 747–762

[28] Ulu E, Gecer Ulu N, Hsiao W, Nelaturi S. Manufacturability oriented model correction and build direction optimization for additive manufacturing. Journal of Mechanical Design, 2020, 142(6): 062001

[29] Wang C F, Qian X P. Simultaneous optimization of build orientation and topology for additive manufacturing. Additive Manufacturing, 2020, 34: 101246

[30] Cheng W, Fuh J Y H, Nee A Y C, Wong Y S, Loh H T, Miyazawa T. Multi-objective optimization of part-building orientation in stereolithography. Rapid Prototyping Journal, 1995, 1(4): 12–23

[31] Pham D T, Dimov S S, Gault R S. Part orientation in stereolithography. The International Journal of Advanced Manufacturing Technology, 1999, 15(9): 674–682

[32] West A P, Sambu S P, Rosen D W. A process planning method for improving build performance in stereolithography. Computer-Aided Design, 2001, 33(1): 65–79

[33] QieL F, Jing S K, LianR C, ChenY, LiuJ H. Quantitative suggestions for build orientation selection. The International Journal of Advanced Manufacturing Technology, 2018, 98(5–8): 1831–1845

[34] CanellidisV, Giannatsis J, DedoussisV. Genetic-algorithm-based multi-objective optimization of the build orientation in stereolithography. The International Journal of Advanced Manufacturing Technology, 2009, 45(7–8): 714–730

[35] Mele M, Campana G. Sustainability-driven multi-objective evolutionary orienting in additive manufacturing. Sustainable Production and Consumption, 2020, 23: 138–147

[36] Phatak A M, Pande S S. Optimum part orientation in rapid prototyping using genetic algorithm. Journal of Manufacturing Systems, 2012, 31(4): 395–402

[37] Singhal S K, Jain P K, Pandey P M, Nagpal A K. Optimum part deposition orientation for multiple objectives in SL and SLS prototyping. International Journal of Production Research, 2009, 47(22): 6375–6396

[38] Padhye N, Deb K. Multi-objective optimisation and multi-criteria decision making in SLS using evolutionary approaches. Rapid Prototyping Journal, 2011, 17(6): 458–478

[39] Zhang Y C, Bernard A, Gupta R K, Harik R. Feature based building orientation optimization for additive manufacturing. Rapid Prototyping Journal, 2016, 22(2): 358–376

[40] Al-Ahmari A M, Abdulhameed O, Khan A A. An automatic and optimal selection of parts orientation in additive manufacturing. Rapid Prototyping Journal, 2018, 24(4): 698–708

[41] Zhang Y C, Harik R, Fadel G, Bernard A. A statistical method for build orientation determination in additive manufacturing. Rapid Prototyping Journal, 2019, 25(1): 187–207

[42] Qin Y C, Qi Q F, Shi P Z, Scott P J, Jiang X Q. Automatic determination of part build orientation for laser powder bed fusion. Virtual and Physical Prototyping, 2021, 16(1): 29–49

[43] Paul R, Anand S. Optimization of layered manufacturing process for reducing form errors with minimal support structures. Journal of Manufacturing Systems, 2015, 36(7): 231–243

[44] Chowdhury S, Mhapsekar K, Anand S. Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process. Journal of Manufacturing Science and Engineering, 2018, 140(3): 031009

[45] Xu J H, Sheng H S, Zhang S Y, Tan J R, Deng J L. Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number. Frontiers of Mechanical Engineering, 2021, 16(1): 133–150

[46] Mahmood M A, Visan A I, Ristoscu C, Mihailescu I N. Artificial neural network algorithms for 3D printing. Materials (Basel), 2021, 14(1): 163

[47] Goh G D, Sing S L, Yeong W Y. A review on machine learning in 3D printing: applications, potential, and challenges. Artificial Intelligence Review, 2021, 54(1): 63–94

[48] Zhang Q F, Li H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712–731

[49] ChenS J, Hwang C L. Fuzzy Multiple Attribute Decision-Making Methods and Application. Berlin: Springer, 1992

[50] Xia P P, Zhang L, Li F Z. Learning similarity with cosine similarity ensemble. Information Sciences, 2015, 307: 39–52

[51] Nezarat H, Sereshki F, Ataei M. Ranking of geological risks in mechanized tunneling by using fuzzy analytical hierarchy process (FAHP). Tunnelling and Underground Space Technology, 2015, 50: 358–364

[52] Moroni G, Syam W P, Petrò S. Functionality-based part orientation for additive manufacturing. Procedia CIRP, 2015, 36: 217–222

[53] Ding D H, Pan Z X, Cuiuri D, Li H J, Larkin N, van Duin S. Automatic multi-direction slicing algorithms for wire based additive manufacturing. Robotics and Computer-Integrated Manufacturing, 2016, 37: 139–150

[54] Xu J H, Sheng H S, Zhan J T, Zhang S Y, Tan J R. Assembly-free design for additive manufacturing of articulated components based on layered precision assignment. International Journal of Computer Integrated Manufacturing, 2022, 35(9): 909–926

[55] Strano G, Hao L, Everson R M, Evans K E. Surface roughness analysis, modelling and prediction in selective laser melting. Journal of Materials Processing Technology, 2013, 213(4): 589–597

[56] Triantaphyllou A, Giusca C L, Macaulay G D, Roerig F, Hoebel M, Leach R K, Tomita B, Milne K A. Surface texture measurement for additive manufacturing. Surface Topography: Metrology and Properties, 2015, 3(2): 024002

[57] Tian Y, Tomus D, Rometsch P, Wu X H. Influences of processing parameters on surface roughness of HastelloyX produced by selective laser melting. Additive Manufacturing, 2017, 13: 103–112

[58] Yan X C, Gao S H, Chang C, Huang J, Khanlari K, Dong D D, Ma W Y, Fenineche N, Liao H L, Liu M. Effect of building directions on the surface roughness, microstructure, and tribological properties of selective laser melted Inconel 625. Journal of Materials Processing Technology, 2021, 288: 116878

[59] AlghamdiA, Downing D, McMillanM, BrandtM, QianM, LearyM. Experimental and numerical assessment of surface roughness for Ti6Al4V lattice elements in selective laser melting. The International Journal of Advanced Manufacturing Technology, 2019, 105(1–4): 1275–1293

[60] Balbaa M, Mekhiel S, Elbestawi M, McIsaac J. On selective laser melting of Inconel 718: densification, surface roughness, and residual stresses. Materials & Design, 2020, 193: 108818

[61] Feng S C, Kamat A M, Sabooni S, Pei Y T. Experimental and numerical investigation of the origin of surface roughness in laser powder bed fused overhang regions. Virtual and Physical Prototyping, 2021, 16(sup1): S66–S84

[62] Möller T, Trumbore B. Fast, minimum storage ray-triangle intersection. Journal of Graphics Tools, 1997, 2(1): 21–28

[63] Calignano F. Design optimization of supports for overhanging structures in aluminum and titanium alloys by selective laser melting. Materials & Design, 2014, 64: 203–213

[64] Liu J K, Chen Q, Liang X, To A C. Manufacturing cost constrained topology optimization for additive manufacturing. Frontiers of Mechanical Engineering, 2019, 14(2): 213–221

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