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Mechanical property estimation for additive manufacturing parts with supports

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Abstract

Bridging in additive manufacturing (AM) parts occur between two points without any support from below. If the material extruded between these points is dangled (i.e., not horizontal), this is called poor bridging. This paper investigates the effects of AM process parameters and distance between supports on not only poor bridging but also mechanical properties of parts manufactured using AM. Two estimated models (EMs) are computed to predict ultimate stress and strain given these design parameters. To do this, central composite design (CCD) experimental design method is employed to obtain experiment sets based on design parameters. The number of tests is then increased using a systematic sampling technique to obtain the EMs with accurate prediction quality. Tensile experiments are then carried out for all the experiments. Finally, the obtained EMs are integrated into a process optimization test case, in which design parameters are automatically determined in order to achieve mechanical constraints based on the ultimate stress or strain of the printed parts.

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Notes

  1. https://en.wikipedia.org/wiki/3D_printing

  2. https://www.mathworks.com/products/matlab.html

  3. https://support.microsoft.com/en-US

References

  1. Mohan N, Senthil P, Vinodh S, Jayanth N (2017) A review on composite materials and process parameters optimisation for the fused deposition modelling process. Virtual Phys Prototyp 12(1):47–59

    Article  Google Scholar 

  2. Bhatia A, Sehgal AK (2021) Additive manufacturing materials, methods and applications: a review. Mater Today: Proc

  3. Rasiya G, Shukla A, Saran K (2021) Additive manufacturing-a review. Mater Today: Proc 47:6896–6901. International conference on advances in design, materials and manufacturing

  4. Ali SF, Malik FM, Kececi EF, Bal B (2019) Optimization of additive manufacturing for layer sticking and dimensional accuracy. In: Additive manufacturing technologies from an optimization perspective, IGI Global, pp. 185–198

  5. Wahab Hashmi A, Singh Mali H, Meena A (2021) Improving the surface characteristics of additively manufactured parts: a review. Mater Today: Proc

  6. Ghungrad S, Gould B, Soltanalian M, Wolff SJ, Haghighi A (2021) Model-based deep learning for additive manufacturing: new frontiers and applications. Manuf Lett 29:94–98

    Article  Google Scholar 

  7. Blakey-Milner B, Gradl P, Snedden G, Brooks M, Pitot J, Lopez E, Leary M, Berto F, du Plessis A (2021) Metal additive manufacturing in aerospace: a review. Mater Des 209:110008

    Article  Google Scholar 

  8. Javaid M, Haleem A, Singh RP, Suman R, Rab S (2021) Role of additive manufacturing applications towards environmental sustainability. Adv Ind Eng Polym Res 4(4):312–322. 3D Printing of Polymers

  9. Shrinivas Mahale R, Shamanth V, Hemanth K, Nithin S, Sharath P, Shashanka R, Patil A, Shetty D (2022) Processes and applications of metal additive manufacturing. Mater Today: Proc 54:228–233. 5th International conference on advanced research in mechanical, materials and manufacturing engineering-2021

  10. Mojumder S, Gan Z, Li Y, Amin AA, Liu WK (2023) Linking process parameters with lack-of-fusion porosity for laser powder bed fusion metal additive manufacturing. Addit Manuf 68:103500. https://doi.org/10.1016/j.addma.2023.103500. https://www.sciencedirect.com/science/article/pii/S2214860423001136

  11. Raigar J, Sharma VS, Srivastava S, Chand R, Singh J (2020) A decision support system for the selection of an additive manufacturing process using a new hybrid MCDM technique. Sādhanā 45(1):101. https://doi.org/10.1007/s12046-020-01338-w

    Article  Google Scholar 

  12. Mukherjee T, Zhang W, DebRoy T (2017) An improved prediction of residual stresses and distortion in additive manufacturing. Comput Mater Sci 126:360–372. https://doi.org/10.1016/j.commatsci.2016.10.003. https://www.sciencedirect.com/science/article/pii/S0927025616304980

  13. Tamir TS, Xiong G, Fang Q, Yang Y, Shen Z, Zhou M, Jiang J (2022) Machine-learning-based monitoring and optimization of processing parameters in 3D printing. Int J Comput Integr Manuf 36(9):1362–1378. https://doi.org/10.1080/0951192X.2022.2145019

    Article  Google Scholar 

  14. Tamir TS, Xiong G, Fang Q, Dong X, Shen Z, Wang F-Y (2022) A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach. Int J Adv Manuf Technol 120(3):2777–2791. https://doi.org/10.1007/s00170-021-08332-4

    Article  Google Scholar 

  15. Korneev S, Wang Z, Thiagarajan V, Nelaturi S (2020) Fabricated shape estimation for additive manufacturing processes with uncertainty. Comput-Aided Des 127:102852. https://doi.org/10.1016/j.cad.2020.102852. https://www.sciencedirect.com/science/article/pii/S0010448520300452

  16. Atakok G, Kam M, Koc HB (2022) Tensile, three-point bending and impact strength of 3D printed parts using PLA and recycled PLA filaments: a statistical investigation. J Mater Res Technol 18:1542–1554. https://doi.org/10.1016/j.jmrt.2022.03.013. https://www.sciencedirect.com/science/article/pii/S2238785422003192

  17. Garzon-Hernandez S, Garcia-Gonzalez D, Jérusalem A, Arias A (2020) Design of FDM 3D printed polymers: an experimental-modelling methodology for the prediction of mechanical properties. Materials & Design 188:108414. https://doi.org/10.1016/j.matdes.2019.108414. https://www.sciencedirect.com/science/article/pii/S0264127519308524

  18. Jiang J, Xu X, Stringer J (2018) Support structures for additive manufacturing: a review. J Manuf Mater Process 2(4)

  19. Wang Z, Zhang Y, Tan S, Ding L, Bernard A (2021) Support point determination for support structure design in additive manufacturing. Addit Manuf 47. https://doi.org/10.1016/j.addma.2021.102341. https://www.sciencedirect.com/science/article/pii/S2214860421004991

  20. Zhou L, Sigmund O, Zhang W (2021) Self-supporting structure design with feature-driven optimization approach for additive manufacturing. Comput Methods Appl Mech Eng 386:114110. https://doi.org/10.1016/j.cma.2021.114110. https://www.sciencedirect.com/science/article/pii/S0045782521004412

  21. Mirzendehdel AM, Suresh K (2016) Support structure constrained topology optimization for additive manufacturing. Comput-Aided Des 81:1–13. https://doi.org/10.1016/j.cad.2016.08.006. https://www.sciencedirect.com/science/article/pii/S0010448516300951

  22. Mirzendehdel AM, Behandish M, Nelaturi S (2021) Optimizing build orientation for support removal using multi-axis machining. Comput Graph 99:247–258. https://doi.org/10.1016/j.cag.2021.07.011. https://www.sciencedirect.com/science/article/pii/S0097849321001424

  23. Mirzendehdel AM, Behandish M, Nelaturi S (2022) Topology optimization for manufacturing with accessible support structures. Comput-Aided Des 142:103117. https://doi.org/10.1016/j.cad.2021.103117. https://www.sciencedirect.com/science/article/pii/S0010448521001287

  24. Feng R, Li X, Zhu L, Thakur A, Wei X (2021) An improved two-level support structure for extrusion-based additive manufacturing. Robot Comput -Integr Manuf 67:101972. https://doi.org/10.1016/j.rcim.2020.101972. https://www.sciencedirect.com/science/article/pii/S0736584519308324

  25. Cheng B, Chou K (2020) A numerical investigation of support structure designs for overhangs in powder bed electron beam additive manufacturing. J Manuf Process 49:187–195. https://doi.org/10.1016/j.jmapro.2019.11.018. https://www.sciencedirect.com/science/article/pii/S1526612519303998

  26. Yang D, Pan C, Zhou Y, Han Y (2022) Optimized design and additive manufacture of double-sided metal mirror with self-supporting lattice structure. Mater Des 219:110759. https://doi.org/10.1016/j.matdes.2022.110759. https://www.sciencedirect.com/science/article/pii/S0264127522003811

  27. Zhang J, Cao Q, Lu WF (2022) A review on design and removal of support structures in metal additive manufacturing. Mater Today: Proc 70:407–411. The international conference on additive manufacturing for a better world (AMBW 2022). https://doi.org/10.1016/j.matpr.2022.09.277. https://www.sciencedirect.com/science/article/pii/S2214785322060886

  28. Li Y, Tang K, He D, Wang X (2021) Multi-axis support-free printing of freeform parts with lattice infill structures. Comput-Aided Des 133:102986. https://doi.org/10.1016/j.cad.2020.102986. https://www.sciencedirect.com/science/article/pii/S0010448520301792

  29. Vaissier B, Pernot J-P, Chougrani L, Véron P (2019) Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing. Comput-Aided Des 110:11–23. https://doi.org/10.1016/j.cad.2018.12.007. https://www.sciencedirect.com/science/article/pii/S001044851830277X

  30. Feng R, Jiang J, Sun Z, Thakur A, Wei X (2021) A hybrid of genetic algorithm and particle swarm optimization for reducing material waste in extrusion-based additive manufacturing. Rapid Prototyp J 27(10):1872–1885

    Article  Google Scholar 

  31. Zhang N, Zhang L-C, Chen Y, Shi Y-S (2019) Local barycenter based efficient tree-support generation for 3D printing. Comput-Aided Des 115:277–292

    Article  Google Scholar 

  32. Zhou Y, Lu H, Ren Q, Li Y (2019) Generation of a tree-like support structure for fused deposition modelling based on the l-system and an octree. Graph Models 101:8–16

    Article  Google Scholar 

  33. Armanfar A, Gunpinar E (2023) G-Lattices: a novel lattice structure and its generative synthesis under additive manufacturing constraints. J Mech Des 145(1)

  34. Liu W, Song H, Huang C (2020) Maximizing mechanical properties and minimizing support material of PolyJet fabricated 3D lattice structures. Addit Manuf 35:101257. https://doi.org/10.1016/j.addma.2020.101257. https://www.sciencedirect.com/science/article/pii/S2214860420306291

  35. Gunpinar E, Armanfar A (2022) Helical5AM: five-axis parametrized helical additive manufacturing. J Mater Process Technol 304:117565

  36. Moetazedian A, Allum J, Gleadall A, Mele E, Silberschmidt VV (2021) MaTrEx AM: a new hybrid additive manufacturing process to selectively control mechanical properties. Addit Manuf 47:102337. https://doi.org/10.1016/j.addma.2021.102337. https://www.sciencedirect.com/science/article/pii/S2214860421004954

  37. Gunpinar E, Gunpinar S (2018) A shape sampling technique via particle tracing for CAD models. Graph Models 96:11–29

    Article  MathSciNet  Google Scholar 

  38. Gunpinar E, Coskun UC, Ozsipahi M, Gunpinar S (2019) A generative design and drag coefficient prediction system for sedan car side silhouettes based on computational fluid dynamics. Comput-Aided Des 111:65–79

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Dr. Emrecan Söylemez and Dr. Yunus Ziya Arslan for their valuable discussions. We would like to express our gratitude to A. Alper Tasmektepligil for machine learning works and Ismail Cem Akgun and Mert Gümüç for their assistance in the tensile tests. This research was supported by Scientific Research Center of Istanbul Technical University (Project Number: 45085)

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  1. Istanbul Technical University, Inonu Caddesi, No:65, Gumussuyu, 34437, Istanbul, Turkey

    Emre Günaydın & Erkan Gunpinar

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  1. Emre Günaydın
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  2. Erkan Gunpinar
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EG: conceptualization, methodology, implementation, validation, writing. EG: conceptualization, methodology, writing.

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Correspondence to Emre Günaydın.

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Günaydın, E., Gunpinar, E. Mechanical property estimation for additive manufacturing parts with supports. Int J Adv Manuf Technol 129, 4031–4044 (2023). https://doi.org/10.1007/s00170-023-12482-y

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