A Review of Machine Learning Algorithm Development Methods for Predicting the Mechanical Properties of 3D Printed Components and Optimizing Parameters
کلمات کلیدی:
Additive Manufacturing, Design and Production of Complex Components, Process Parameter Control, Mechanical Propertiesچکیده
Additive Manufacturing (AM) technology has been widely used in various industries due to its high flexibility in designing and producing complex components. However, the precise control of process parameters and the prediction of the mechanical properties of printed components have always been challenging. This paper reviews the role of Machine Learning (ML) in optimizing printing parameters (such as laser power, scan speed, and layer thickness) and predicting the quality of components. Predictive models for defects such as porosity and cracking are also analyzed. A comparison of data-driven approaches, including supervised learning and reinforcement learning, demonstrates that each has unique advantages under specific conditions. Finally, a perspective on the future integration of AM and Artificial Intelligence (AI) in the design of new materials is presented.
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1. Ruiz-Alonso, S.; Lafuente-Merchan, M.; Ciriza, J.; Saenz-del-Burgo, L.; Pedraz, J.L. Tendon tissue engineering: Cells,
growth factors, scaffolds and production techniques. J. Control. Release 2021, 333, 448–486.
2. Roseti, L.; Parisi, V.; Petretta, M.; Cavallo, C.; Desando, G.; Bartolotti, I.; Grigolo, B. Scaffolds for Bone Tissue
Engineering: State of the art and new perspectives. Mater. Sci. Eng. C 2017, 78, 1246–1262.
3. Preethi Soundarya, S.; Haritha Menon, A.; Viji Chandran, S.; Selvamurugan, N. Bone tissue engineering: Scaffold
preparation using chitosan and other biomaterials with different design and fabrication techniques. Int. J. Biol. Macromol.
2018, 119, 1228–1239.
4. Melchels, F.P.W.; Domingos, M.A.N.; Klein, T.J.; Malda, J.; Bartolo, P.J.; Hutmacher, D.W. Additive manufacturing of
tissues and organs. Prog. Polym. Sci. 2012, 37, 1079–1104.
5. Qu, H. Additive manufacturing for bone tissue engineering scaffolds. Mater. Today Commun. 2020, 24, 101024.
6. Adeniran, O.; Osa-uwagboe, N.; Cong, W.; Ramoni, M. Fabrication Temperature-Related Porosity Effects on the
Mechanical Properties of Additively Manufactured CFRP Composites. J. Comp. Sci. 2023, 7, 12.
7. Karuth, A.; Alesadi, A.; Xia, W.; Rasulev, B. Predicting glass transition of amorphous polymers by application of
cheminformatics and molecular dynamics simulations. Polymer 2021, 218, 123495.
8. Li, X.; Yu, H.; Feng, H.; Zhang, S.; Fu, Y. Design and Control for WLR-3P: A Hydraulic Wheel-Legged Robot. Cyborg
Bionic Syst. 2023, 4, 25.
9. Atay, I.; Yilgör, E.; Sürme, S.; Kavakli, I.H.; Yilgör, I. Critical role of the composition of the cell culture medium on cell
attachment and viability on PLA biocomposite scaffolds under in vitro assay conditions. Polymer 2024, 297, 126823.
10. Senatov, F.S.; Niaza, K.V.; Zadorozhnyy, M.Y.; Maksimkin, A.V.; Kaloshkin, S.D.; Estrin, Y.Z. Mechanical properties
and shape memory effect of 3D-printed PLA-based porous scaffolds. J. Mech. Behav. Biomed. Mater. 2016, 57, 139–
148.
11. Udu, A.G.; Osa-uwagboe, N.; Olusanmi, A.; Aremu, A.; Khaksar, M.; Dong, H. A machine learning approach to
characterise fabrication porosity effects on the mechanical properties of additively manufactured thermoplastic
Composites. J. Reinf. Plast. Compos. 2024.
12. Czajka, A.; Plichta, A.; Bulski, R.; Pomilovskis, R.; Iuliano, A.; Cygan, T.; Ryszkowska, J. PLA reinforced with modified
chokeberry pomace and beetroot pulp fillers. Effect of oligomeric chain extender on the properties of biocomposites.
Polymer 2023, 289, 126472.
13. Farah, S.; Anderson, D.G.; Langer, R. Physical and mechanical properties of PLA, and their functions in widespread
applications—A comprehensive review. Adv. Drug Deliv. Rev. 2016, 107, 367–392.
14. Nevado, P.; Lopera, A.; Bezzon, V.; Fulla, M.R.; Palacio, J.; Zaghete, M.A.; Biasotto, G.; Montoya, A.; Rivera, J.;
Robledo, S.; et al. Preparation and in vitro evaluation of PLA/biphasic calcium phosphate filaments used for fused
deposition modelling of scaffolds. Mater. Sci. Eng. C 2020, 114, 111013.
15. Chen, Y. ; Guo, Y. ; Xie, B.; Jin, F.; Ma, L.; Zhang, H.; Li, Y.; Chen, X.; Hou, M.; Gao, J.; et al. Lightweight and driftfree magnetically actuated millirobots via asymmetric laser-induced graphene. Nat. Commun. 2024, 15, 4334.
16. Ranjan, N.; Singh, R.; Ahuja, I.S. Preparation of Partial Denture with Nano HAp-PLA Composite Under Cryogenic
Grinding Environment Using 3D Printing. In Materials Science and Materials Engineering Encyclopedia of Renewable
and Sustainable Materials; Elsevier Ltd.: Amsterdam, The Netherlands, 2020; Volume 4.
17. Ignjatović, N.; Tomić, S.; Dakić, M.; Miljković, M.; Plavšić, M.; Uskoković, D. Synthesis and properties of
hydroxyapatite/poly-L-lactide composite biomaterials. Biomaterials 1999, 20, 809–816.
18. Xiao, L.; Liu, H.; Huang, H.; Wu, S.; Xue, L.; Geng, Z.; Cai, L.; Yan, F. 3D nanofiber scaffolds from 2D electrospun
membranes boost cell penetration and positive host response for regenerative medicine. J. Nanobiotechnol. 2024, 22,
322.
19. Yüksel, N.; Börklü, H.R.; Sezer, H.K.; Canyurt, O.E. Review of artificial intelligence applications in engineering design
perspective. Eng. Appl. Artif. Intell. 2023, 118, 105697.
20. Suwardi, A.; Wang, F.K.; Xue, K.; Han, M.Y.; Teo, P.; Wang, P.; Wang, S.; Liu, Y.; Ye, E.; Li, Z.; et al. Machine
Learning-Driven Biomaterials Evolution. Adv. Mater. 2022, 34, 2102703.
21. Sharma, S.; Gupta, V.; Mudgal, D.; Srivastava, V. Predicting biomechanical properties of additively manufactured
polydopamine coated poly lactic acid bone plates using deep learning. Eng. Appl. Artif. Intell. 2023, 124, 106587.
22. Xue, X.; Zhang, H.; Liu, H.; Wang, S.; Li, J.; Zhou, Q.; Chen, X.; Ren, X.; Jing, Y.; Deng, Y.; et al. Rational Design of
Multifunctional CuS Nanoparticle-PEG Composite Soft Hydrogel-Coated 3D Hard Polycaprolactone Scaffolds for
Efficient Bone Regeneration. Adv. Funct. Mater. 2022, 32, 2202470.
23. Alakent, B.; Soyer-Uzun, S. Implementation of Statistical Learning Methods to Develop Guidelines for the Design of
PLA-Based Composites with High Tensile Strength Values. Ind. Eng. Chem. Res. 2019, 58, 3478–3489.
24. Thakur, V.; Kumar, R.; Kumar, R.; Singh, R.; Kumar, V. Hybrid additive manufacturing of highly sustainable Polylactic
acid -Carbon Fiber-Polylactic acid sandwiched composite structures: Optimization and machine learning. J. Thermoplast.
Compos. Mater. 2024, 37, 466–492.
25. Munir, N.; McMorrow, R.; Mulrennan, K.; Whitaker, D.; McLoone, S.; Kellomäki, M.; Talvitie, E.; Lyyra, I.; McAfee,
M. Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid.
Polymers 2023, 15, 3566.
26. Zhang, B.; Wang, L.; Song, P.; Pei, X.; Sun, H.; Wu, L.; Zhou, C.; Wang, K.; Fan, Y.; Zhang, X. 3D printed bone tissue
regenerative PLA/HA scaffolds with comprehensive performance optimizations. Mater. Des. 2021, 201, 109490.
27. Nie, T.; Xue, L.; Ge, M.; Ma, H.; Zhang, J. Fabrication of poly (L-lactic acid) tissue engineering scaffolds with precisely
controlled gradient structure. Mater. Lett. 2016, 176, 25–28.
28. Oladapo, B.I.; Zahedi, S.A.; Adeoye, A.O.M. 3D printing of bone scaffolds with hybrid biomaterials. Compos. B Eng.
2019, 158, 428–436.
29. ASTM D638-22; Standard Test Method for Tensile Properties of Plastics 1. Annual Book of ASTM Standards. ASTM
International: West Conshohocken, PA, USA, 2019.
30. Lee, D.; Kwon, H.J.; Yang, S.; Kim, M.S. Fabrication, testing, and analysis of sandwich structure with composite skin
and additive manufactured core. J. Reinf. Plast. Compos. 2021, 40, 654–664.
31. López-Vázquez, C.; Hochsztain, E. Extended and updated tables for the Friedman rank test. Commun. Stat. Theory
Methods 2019, 48, 268–281.
32. Bentéjac, C.; Csörgő, A.; Martínez-Muñoz, G. A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev.
2021, 54, 1937–1967.
33. Osa-uwagboe, N.; Udu, A.G.; Ghalati, M.K.; Silberschmidt, V.V.; Aremu, A.; Dong, H.; Demirci, E. A machine learningenabled prediction of damage properties for fiber-reinforced polymer composites under out-of-plane loading. Eng. Struct.
2024, 308, 117970.
