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Nesting of 3D irregular shaped objects applied to powder-based additive manufacturing

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Abstract

This paper proposes a 3D nesting algorithm which combines the flower pollination algorithm (FPA) with the oriented bounding box (OBB) collision detection to solve the 3D packing of irregular shaped objects for powder-based additive manufacturing. In the powder-based 3D printing process, stacking multiple models most compactly in the build volume is an important task because no support structure is needed. Given a fixed printing area, the post-nesting build height directly impacts the printing cost and efficiency. To reduce the printing cost, 3D models must be packed as closely as possible and the build height must be minimized. This has been found to be a combinatorial optimization and an NP-hard problem. We propose to use the most recent and effective meta-heuristic optimization algorithm FPA, combined with the collision detection of printed objects set up as the optimization constraint using the OBB tree, to find the near-optimal 3D nesting solution. In FPA optimization, a significant amount of time is spent on collision detection. This paper uses the safety clearance distance to adaptively reduce the OBB tree subdivision, hence significantly reducing the computation of collision detection. As a result, the proposed method finds the model positions and rotations of the global solution, and generates the near-optimal solution in reasonable time. Finally, our method is compared with state-of-the-art commercial softwares. The results show that the proposed method produces lower build height with better efficiency. For real-world complex engine parts (30 different STL models and a total of 192,018 triangles), the computation time is only 160 s, and the build height is 12.4% and 13% better than the results from Netfabb and Magics, respectively.

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References

  1. Hague R, Mansour S, Saleh N (2004) Material and design considerations for rapid manufacturing. Int J Prod Res 42(22):4691–4708

    Article  Google Scholar 

  2. Gibson IG (2014) Additive manufacturing technologies 3D printing, rapid prototyping, and direct digital manufacturing. Springer, Berlin, p 414

    Google Scholar 

  3. Tuck CJ, Hague RJ, Ruffo M, Ransley M, Adams P (2008) Rapid manufacturing facilitated customization. Int J Comput Integr Manuf 21(3):245–258

    Article  Google Scholar 

  4. Ruffo M, Hague R (2007) Cost estimation for rapid manufacturing’simultaneous production of mixed components using laser sintering. Proc Inst Mech Eng, Part B: J Eng Manuf 221(11):1585–1591

    Article  Google Scholar 

  5. Hur SM, Choi KH, Lee SH, Chang PK (2001) Determination of fabricating orientation and packing in SLS process. J Mater Process Technol 112(2-3):236–243

    Article  Google Scholar 

  6. Nyaluke A, Nasser B, Leep HR, Parsaei HR (1996) Rapid prototyping work space optimization. Comput Ind Eng 31(1-2):103–106

    Article  Google Scholar 

  7. Baumers M, Tuck C, Wildman R, Ashcroft I, Rosamond E, Hague R (2013) Transparency built-in: energy consumption and cost estimation for additive manufacturing. J Ind Ecol 17(3):418–431

    Article  Google Scholar 

  8. Baumers M, Beltrametti L, Gasparre A, Hague R (2017) Informing additive manufacturing technology adoption: total cost and the impact of capacity utilisation. Int J Prod Res 55(23):6957–6970

    Article  Google Scholar 

  9. Weller C, Kleer R, Piller FT (2015) Economic implications of 3D printing: Market structure models in light of additive manufacturing revisited. Int J Prod Econ 164:43–56

    Article  Google Scholar 

  10. Garey MR, Johnson DS (1979) Computers and Intractability - a guide to the theory of NP-completeness. W. H. Freeman & Co., New York, pp 109–118

    MATH  Google Scholar 

  11. Ikonen I, Biles WE, Kumar A, Wissel JC, & Ragade RK (1997) A genetic algorithm for packing three-dimensional non-convex objects having cavities and holes. in ICGA, pp591-598.

  12. Stoyan YG, Gil NI, Scheithauer G, Pankratov A, Magdalina I (2005) Packing of convex polytopes into a parallelepiped. Optimization 54(2):215–235

    Article  MathSciNet  Google Scholar 

  13. Gogate AS, Pande SS (2008) Intelligent layout planning for rapid prototyping. Int J Prod Res 46(20):5607–5631

    Article  Google Scholar 

  14. Egeblad J, Nielsen BK, Brazil M (2009) Translational packing of arbitrary polytopes. Comput Geom 42(4):269–288

    Article  MathSciNet  Google Scholar 

  15. Wu S, Kay M, King R, Vila-Parrish A, & Warsing D (2014)Multi-objective optimization of 3D packing problem in additive manufacturing. In IIE annual conference. Proceedings. pp 1485.

  16. Liu X, Liu JM, Cao AX (2015)HAPE3D—a new constructive algorithm for the 3D irregular packing problem. Front Inform Tech Elect Eng 16(5):380–390

    Article  Google Scholar 

  17. Chernov N, Stoyan Y, Romanova T (2010) Mathematical model and efficient algorithms for object packing problem. Comput Geom Theory Appl 43(5):535–553

    Article  MathSciNet  Google Scholar 

  18. Litvinchev I, Pankratov A, Romanova T (2019) 3D irregular packing in an optimized cuboid container. IFAC-PapersOnLine 52(13):2014–2019

    Article  Google Scholar 

  19. Gardan J (2016) Additive manufacturing technologies: state of the art and trends. Int J Prod Res 54(10):3118–3132

    Article  Google Scholar 

  20. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press. pp 1-16.

  21. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  Google Scholar 

  22. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, SCI 284, pp. 65-74.

  23. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84

    Article  Google Scholar 

  24. Blum C (2005) Ant colony optimization: Introduction and recent trends. Phys Life Rev 2(4):353–373

    Article  Google Scholar 

  25. Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237

    Article  MathSciNet  Google Scholar 

  26. Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192–203

    Article  Google Scholar 

  27. Canellidis V, Dedoussis V, Mantzouratos N, Sofianopoulou S (2006)Pre-processing methodology for optimizing stereolithography apparatus build performance. Comput Ind 57(5):424–436

    Article  Google Scholar 

  28. Canellidis V, Giannatsis J, & Dedoussis V (2010) Effective nesting of layer manufacturing fabricated parts using a genetic algorithm and a bottom-left ray casting procedure. In IEEM, IEEE International Conference, pp 547-551.

  29. Lorensen WE, Cline HE (1987) Marching cubes: A high resolution 3D surface construction algorithm. ACM Siggraph Comp Graph 21(4):163–169

    Article  Google Scholar 

  30. Gottschalk S, Lin MC, & Manocha D (1996). OBBTree: a hierarchical structure for rapid interference detection. In Computer Graphics (SIGGRAPH 96 Proceedings), pp 171–180.

  31. Bergen GVD (1997) Efficient collision detection of complex deformable models using AABB trees. J Graph Tools 2(4):1–13

    Article  Google Scholar 

  32. Ray T, Liew KM (2002) A swarm metaphor for multiobjective design optimization. Eng Opt 34(2):141–153

    Article  Google Scholar 

  33. Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844

    Article  MathSciNet  Google Scholar 

  34. Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys Rev E 49(5):4677–4683

    Article  Google Scholar 

  35. Draa A (2015) On the performances of the flower pollination algorithm–qualitative and quantitative analyses. Appl Soft Comput 34:349–371

    Article  Google Scholar 

  36. Ericson C (2004)Real-time collision detection. CRC Press, New York, pp 1–2

    Book  Google Scholar 

  37. Autodesk Netfabb. https://www.autodesk.com/products/netfabb.

  38. Materialise Magics. https://www.materialise.com/en/software/magics.

  39. Fabpilot. https://www.fabpilot.com/.

  40. Atef A (2018) V6 Engine. GrabCAD Community Library. https://grabcad.com/library/v-6-engine-12-valve-1/details?folder_id=4802705

  41. Araújo LJ, Özcan E, Atkin JA, Baumers M (2019) Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset. Int J Prod Res 57(18):5920–5934

    Article  Google Scholar 

  42. Mansaram MV, Chatterjee S, Dinbandhu, Sahu AK, Abhishek K, Mahapatra SS (2021) Analysis of dimensional accuracy of ABS M30 built parts using FDM process. Recent Adv Mech Infrastruct: Proceedings of ICRAM 2020:173–181

    Article  Google Scholar 

  43. Sahu AK, Mahapatra SS, Chatterjee S (2017) Optimization of electrical discharge coating process using MOORA based firefly algorithm. Proceedings of the ASME 2017 Gas Turbine India Conference, Bangalore, pp 1–16

    Google Scholar 

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Funding

The authors received the financial support the Ministry of Science and Technology of Taiwan (Grant number [MOST 109-2221-E-194-006]).

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Both authors contribute equally to the theoretical development and experimental design and implementation of this work.

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Correspondence to Hong-Tzong Yau.

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Yau, HT., Hsu, CW. Nesting of 3D irregular shaped objects applied to powder-based additive manufacturing. Int J Adv Manuf Technol 118, 1843–1858 (2022). https://doi.org/10.1007/s00170-021-07954-y

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