Skip to main content
Have a personal or library account? Click to login
Machine Learning Integration in Additive Manufacturing: From Process Optimization to Biomedical Implant Applications Cover

Machine Learning Integration in Additive Manufacturing: From Process Optimization to Biomedical Implant Applications

Open Access
|Sep 2026

References

  1. Scime L, Beuth J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing.2018; 19: 114-126.
  2. Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials & Design. 2016; 95: 431-445.
  3. Tapia G, Elwany A. A review on process monitoring and control in metal-based additive manufacturing. Journal of Manufacturing Science and Engineering. 2014; 136(6): 060801.
  4. Vock S, Kloeden B, Kirchner A, Weissgaerber T, Kieback B. Powders for powder bed fusion: a review. Prog Addit Manuf. 2019;4(4):383-397.
  5. Kumar S, Gopi T, Harikeerthana N, Gupta MK, Gaur V, Krolczyk GM, et al. Machine learning techniques in additive manufacturing: a state-of-the-art review on design, processes and production control. J Intell Manuf. 2023;34(1):21-55.
  6. Fu Y, Downey AR, Yuan L, Zhang T, Pratt A, Balogun Y. Machine learning algorithms for defect detection in metal laser-based additive manufacturing: a review. J Manuf Process. 2022;75:693-710.
  7. Parsazadeh M, Sharma S, Dahotre N. Towards the next generation of machine learning models in additive manufacturing: a review of process dependent material evolution. Prog Mater Sci. 2023;135:101102.
  8. Akbari P, Ogoke F, Kao NY, Meidani K, Yeh CY, Lee W, et al. Melt-poolNet: melt pool characteristic prediction in metal additive manufacturing using machine learning. Addit Manuf. 2022;55:102817.
  9. Liu S, Stebner AP, Kappes BB, Zhang X. Machine learning for knowledge transfer across multiple metals additive manufacturing printers. Addit Manuf. 2021;39:101877.
  10. Xames MD, Torsha FK, Sarwar F. A systematic literature review on recent trends of machine learning applications in additive manufacturing. J Intell Manuf. 2023;34(6):2529-2555.
  11. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5(1):21.
  12. Kalidindi SR. Hierarchical materials informatics: novel analytics for materials data. Amsterdam: Elsevier; 2015.
  13. Jin L, Zhai X, Wang K, Zhang K, Wu D, Nazir A, et al. Big data, machine learning, and digital twin assisted additive manufacturing: a review. Mater Des. 2024;113086.
  14. Saimon AI, Yangue E, Yue X, Kong Z, Liu C. Advancing additive manufacturing through deep learning: a comprehensive review of current progress and future challenges. IISE Trans. 2025:1-24.
  15. Zhang L, et al. Generative AI and large language models in additive manufacturing: a review of current applications and future perspectives. J Manuf Syst. 2024;72:315-332.
  16. Nguyen DC, et al. Federated learning for smart healthcare: a survey on fundamentals, applications, and challenges. IEEE Rev Biomed Eng. 2023;16:22-41.
  17. Zheng H, et al. Machine learning-assisted design and optimization of porous bone scaffolds: a review. Bioprinting. 2023;31:e00271.
  18. Gessert N, et al. Regulatory challenges and methodological opportunities for AI in medical device manufacturing. Nat Biomed Eng. 2023;7(4):412-428.
  19. Ngo TD, Kashani A, Imbalzano G, Nguyen KT, Hui D. Additive manufacturing (3D printing): a review of materials, methods, applications and challenges. Compos Part B Eng. 2018;143:172-196.
  20. Ventola CL. Medical applications for 3D printing: current and projected uses. Pharm Ther. 2014;39(10):704.
  21. Perrot A, editor. 3D printing of concrete: state of the art and challenges of the digital construction revolution. London: ISTE Press; 2019.
  22. Gebler M, Uiterkamp AJS, Visser C. A global sustainability perspective on 3D printing technologies. Energy Policy. 2014;74:158-167.
  23. Shuaib M, et al. Sustainability assessment of additive manufacturing: a review. Sustainable Prod Consump. 2021;26:1008-1022.
  24. Kalidindi SR. Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials. Int Mater Rev. 2015;60(3):150-168.
  25. Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater. 2016;2(1):1-7.
  26. Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T. Accelerated search for materials with targeted properties by adaptive design. Nat Commun. 2016;7(1):1-9.
  27. Scime L, Beuth J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit Manuf. 2018;19:114-126.
  28. Tapia G, Elwany A. A review on process monitoring and control in metal-based additive manufacturing. J Manuf Sci Eng. 2014;136(6):060801.
  29. Baumgartl H, Tomas J, Buettner R, Merkel M. A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Prog Addit Manuf. 2020;5(1):85-103.
  30. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-555.
  31. Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-3572.
  32. Sadeghi S, et al. Explainable AI for design and manufacturing: a review. J Manuf Syst. 2021;58:2-15.
  33. Mommaerts MY. Reconstruction of a subtotal maxillectomy defect using a customized titanium implant in a 4-year-old child: an 8-year follow-up report. Front Surg. 2020;7:28.
  34. Holzinger A, Biemann C, Pattichis CS, Kell DB. What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:1712.09923. 2017.
  35. Chato L, Regentova E. Survey of transfer learning approaches in the machine learning of digital health sensing data. J Pers Med. 2023;13(12):1703.
  36. Moher D, Stewart L, Shekelle P. Implementing PRISMA-P: recommendations for prospective authors. Systematic Reviews. 2016;5:1-2.
  37. Aromataris E, et al. Summarizing systematic reviews: methodological development, content and review of the JBI appropriate tool. JBI Evid Implement. 2015;13(3):132-140.
  38. Zhu Q, Liu Z, Yan J. Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput Mech. 2021;67:619-635.
  39. Chung J, Shen B, Law ACC, Kong ZJ. Reinforcement learning-based defect mitigation for quality assurance of additive manufacturing. J Manuf Syst. 2022;65:822-835.
  40. Sharma R, Raissi M, Guo YB. Physics-informed machine learning for smart additive manufacturing. arXiv preprint arXiv:2407.10761. 2024.
  41. Saimon AI, Yangue E, Yue X, Kong Z, Liu C. Advancing additive manufacturing through deep learning: a comprehensive review of current progress and future challenges. IISE Trans. 2025:1-24.
  42. Adapa VSK, Kalidindi SR, Saldana CJ. Rapid development of metal additive manufacturing using artificial intelligence/machine learning and high-throughput material testing. Annu Rev Mater Res. 2025;55.
  43. Kunchala BKR, Gamini S, Anilkumar TC. Inclusion of IoT technology in additive manufacturing: machine learning-based adaptive bead modeling and path planning for sustainable wire arc additive manufacturing and process optimization. Proc Inst Mech Eng C J Mech Eng Sci. 2023;237(1):120-132.
  44. Abdolahi A, Soroush H, Khodaygan S. Process capability analysis of additive manufacturing process: a machine learning-based predictive model. Rapid Prototyping J. 2025.
  45. Kwon O, Kim HG, Kim W, Kim GH, Kim K. A convolutional neural network for prediction of laser power using melt-pool images in laser powder bed fusion. IEEE Access. 2020;8:23255-23263.
  46. Westphal E, Seitz H. A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Addit Manuf. 2021;41:101965.
  47. Freeman FS, Chechik L, Todd I. Beat the machine (learning): metal additive manufacturing and closed loop control. Phys Educ. 2020;55(5):055012.
  48. Razaviarab N, Sharifi S, Banadaki YM. Smart additive manufacturing empowered by a closed-loop machine learning algorithm. In: Nano-, Bio-, Info-Tech Sensors and 3D Systems III. SPIE; 2019; 10969: 50-57.
  49. Siegkas P. Generating 3D porous structures using machine learning and additive manufacturing. Mater Des. 2022;220:110858.
  50. Staub A, Brunner L, Spierings AB, Wegener K. A machine-learning-based approach to critical geometrical feature identification and segmentation in additive manufacturing. Technologies. 2022;10(5):102.
  51. Karkaria V, Goeckner A, Zha R, Chen J, Zhang J, Zhu Q, et al. Towards a digital twin framework in additive manufacturing: machine learning and bayesian optimization for time series process optimization. J Manuf Syst. 2024;75:322-332.
  52. Paul A, Mozaffar M, Yang Z, Liao WK, Choudhary A, Cao J, et al. A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE. 2019;541-550.
  53. Mozaffar M, Ebrahimi A, Cao J. Toolpath design for additive manufacturing using deep reinforcement learning. arXiv preprint arXiv:2009.14365. 2020.
  54. Liu R, Liu S, Zhang X. A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing. Int J Adv Manuf Technol. 2021;113(7):1943-1958.
  55. Alabi MO, Nixon K, Botef I. A survey on recent applications of machine learning with big data in additive manufacturing industry. Am J Eng Appl Sci. 2018;11(3):1114-1124.
  56. Liu Z, Li M, Weng Y, Qian Y, Wong TN, Tan MJ. Modelling and parameter optimization for filament deformation in 3D cementitious material printing using support vector machine. Compos Part B Eng. 2020;193:108018.
  57. Goncalves DA, Stemmer MR, Pereira M. A convolutional neural network approach on bead geometry estimation for a laser cladding system. Int J Adv Manuf Technol. 2020;106:1811-1821.
  58. Yi Y, Xie R, Yang H. The estimation of the laser point temperature based on CNN (Convolutional Neural Network). IOP Conf Ser Mater Sci Eng. 2020;740(1):012023.
  59. Osswald PV, Mustafa SK, Kaa C, Obst P, Friedrich M, Pfeil M, et al. Optimization of the production processes of powder-based additive manufacturing technologies by means of a machine learning model for the temporal prognosis of the build and cooling phase. Prod Eng. 2020;14:677-691.
  60. Oehlmann P, Osswald P, Blanco JC, Friedrich M, Rietzel D, Witt G. Modeling fused filament fabrication using artificial neural networks. Prod Eng. 2021;15:467-478.
  61. Desai PS, Higgs III CF. Spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning. Metals. 2019;9(11):1176.
  62. Amini M, Chang SI. MLCPM: a process monitoring framework for 3D metal printing in industrial scale. Comput Ind Eng. 2018;124:322-330.
  63. Mahmoudi M, Tapia G, Franco B, Ma J, Arroyave R, Karaman I, et al. On the printability and transformation behavior of nickel-titanium shape memory alloys fabricated using laser powder-bed fusion additive manufacturing. J Manuf Process. 2018;35:672-680.
  64. Chang TW, Liao KW, Lin CC, Tsai MC, Cheng CW. Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning. Int J Adv Manuf Technol. 2021;114:3177-3184.
  65. Ko H, Witherell P, Ndiaye NY, Lu Y. Machine learning based continuous knowledge engineering for additive manufacturing. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). IEEE; 2019. p. 648-654.
  66. Karniadakis GE, et al. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-440.
  67. Razavi N, et al. Closing the loop: generative AI and reinforcement learning for autonomous additive manufacturing. Comput Ind. 2023;148:103905.
  68. Uhlmann E, Pontes RP, Laghmouchi A, Bergmann A. Intelligent pattern recognition of a SLM machine process and sensor data. Procedia CIRP. 2017;62:464-469.
  69. Zhao S, Shi Y, Huang C, Li X, Lu Y, Wu Y, et al. Integrating machine learning into additive manufacturing of metallic biomaterials: a comprehensive review. J Funct Biomater. 2025;16(3):77.
  70. Arivazhagan A, Mani K, Kamarajan BP, V AM, Venugopal NT. Enhancing biocompatibility and mechanical properties of additively manufactured porous gyroid Ti–6Al–4V implants through hydroxyapatite infiltration. Int J Precis Eng Manuf. 2024;25(10):2177-2189.
  71. Peto M, García-Ávila J, Rodriguez CA, Siller HR, da Silva JVL, Ramírez-Cedillo E. Review on structural optimization techniques for additively manufactured implantable medical devices. Front Mech Eng. 2024;10:1353108.
  72. Panahizadeh V, Ghasemi AH, Dadgar Asl Y, Davoudi M. Optimization of LB-PBF process parameters to achieve best relative density and surface roughness for Ti6Al4V samples: using NSGA-II algorithm. Rapid Prototyping J. 2022;28(9):1821-1833.
  73. Wu Y, Lu Y, Zhao M, Bosiakov S, Li L. A critical review of additive manufacturing techniques and associated biomaterials used in bone tissue engineering. Polymers. 2022;14(10):2117.
  74. Wu C, Wan B, Entezari A, Fang J, Xu Y, Li Q. Machine learning-based design for additive manufacturing in biomedical engineering. Int J Mech Sci. 2024;266:108828.
  75. Zhu Q, Liu Z, Yan J. Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput Mech. 2021;67:619-635.
  76. Mohsan AUH, Wei D. Advancements in additive manufacturing of tantalum via the laser powder bed fusion (PBF-LB/M): a comprehensive review. Materials. 2023;16(19):6419.
  77. Aliyu AAA, Poungsiri K, Shinjo J, Panwisawas C, Reed RC, Puncreobutr C, et al. Additive manufacturing of tantalum scaffolds: processing, microstructure and process-induced defects. Int J Refractory Met Hard Mater. 2023;112:106132.
  78. Sungail C, Abid AD. Additive manufacturing of tantalum—a study of chemical and physical properties of printed tantalum. Met Powder Rep. 2020;75(1):28-33.
  79. Harooni A, Iravani M, Khajepour A, King JM, Khalifa A, Gerlich AP. Mechanical properties and microstructures in zirconium deposited by injected powder laser additive manufacturing. Addit Manuf. 2018;22:537-547.
  80. Torun G, Momose T, Sun X, Zhou W, Kikuchi K, Nomura N, et al. Microstructure and mechanical properties of MRI-compatible Zr-9Nb-3Sn alloy fabricated by a laser powder bed fusion process. Addit Manuf. 2022;52:102647.
  81. Yue M, Liu Y, He G, Lian L. Microstructure and mechanical performance of zirconium, manufactured by selective laser melting. Mater Sci Eng A. 2022;840:142900.
  82. Issariyapat A, Huang J, Teramae T, Kariya S, Bahador A, Visuttipitukul P et al. Microstructure refinement and strengthening mechanisms of additively manufactured Ti-Zr alloys prepared from pre-mixed feed-stock. Addit Manuf. 2023;73:103649.
  83. Raheem AA, Hameed P, Whenish R, Elsen RS, Jaiswal AK, Prashanth KG, et al. A review on development of bio-inspired implants using 3D printing. Biomimetics. 2021;6(4):65.
  84. Goh GD, Sing SL, Yeong WY. A review on machine learning in 3D printing: applications, potential, and challenges. Artif Intell Rev. 2021;54(1):63-94.
  85. Özel T, Altay A, Kaftanoğlu B, Leach R, Senin N, Donmez A. Focus variation measurement and prediction of surface texture parameters using machine learning in laser powder bed fusion. J Manuf Sci Eng. 2020;142(1):011008.
  86. Kwon O, Kim HG, Ham MJ, Kim W, Kim GH, Cho JH, et al. A deep neural network for classification of melt-pool images in metal additive manufacturing. J Intell Manuf. 2020;31:375-386.
  87. Kunkel MH, Gebhardt A, Mpofu K, Kallweit S. Quality assurance in metal powder bed fusion via deep-learning-based image classification. Rapid Prototyping J. 2020;26(2):259-266.
  88. Lee CH, Kühn U, Lee SC, Park SJ, Schwab H, Scudino S, et al. Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence. J Alloys Compd. 2021;862:158018.
  89. Meng L, McWilliams B, Jarosinski W, Park HY, Jung YG, Lee J, et al. Machine learning in additive manufacturing: a review. JOM. 2020;72:2363-2377.
  90. Qi X, Chen G, Li Y, Cheng X, Li C. Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering. 2019;5(4):721-729
  91. Wang C, Tan XP, Tor SB, Lim CS. Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit Manuf. 2020;36:101538.
  92. Fu Y, Downey AR, Yuan L, Zhang T, Pratt A, Balogun Y. Machine learning algorithms for defect detection in metal laser-based additive manufacturing: a review. J Manuf Process. 2022;75:693-710.
  93. Zhang Y, Safdar M, Xie J, Li J, Sage M, Zhao YF. A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management. J Intell Manuf. 2023;34(8):3305-3340.
  94. Ciccone F, Bacciaglia A, Ceruti A. Optimization with artificial intelligence in additive manufacturing: a systematic review. J Braz Soc Mech Sci Eng. 2023;45(6):303.
  95. Carroll BE, Otis RA, Borgonia JP, Suh JO, Dillon RP, Shapiro AA, et al. Functionally graded material of 304L stainless steel and inconel 625 fabricated by directed energy deposition: characterization and thermodynamic modeling. Acta Mater. 2016;108:46-54.
  96. Mazzoleni L, et al. Machine learning for surface roughness prediction in 316L steel AM. Int J Adv Manuf Technol. 2022;120:2345-2361.
  97. Mahmoud D, Elbestawi MA. Lattice structures and functionally graded materials applications in additive manufacturing of orthopedic implants: a review. J Manuf Mater Process. 2017;1(2):13.
  98. Li W, Karnati S, Zhang Y, Liou F. Investigating and eliminating powder separation in pre-mixed powder supply for laser metal deposition process. J Mater Process Technol. 2018;254:294-301.
  99. Deng H, et al. Deep learning for defect detection in electron beam melting of Ti-alloys. Addit Manuf. 2021;39:101867.
  100. Zhang X, Li L, Liou F. Additive manufacturing of stainless steel–copper functionally graded materials via Inconel 718 interlayer. J Mater Res Technol. 2021;15:2045-2058.
  101. Chowdhury S, Mhapsekar K, Anand S. Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process. J Manuf Sci Eng. 2018;140(3):031009.
  102. Khanzadeh M, Rao P, Jafari-Marandi R, Smith BK, Tschopp MA, Bian L. Quantifying geometric accuracy with unsupervised machine learning: using self-organizing map on fused filament fabrication additive manufacturing parts. J Manuf Sci Eng. 2018;140(3):031011.
  103. Samie Tootooni M, Dsouza A, Donovan R, Rao PK, Kong Z, Borgesen P. Classifying the dimensional variation in additive manufactured parts from laser-scanned three-dimensional point cloud data using machine learning approaches. J Manuf Sci Eng. 2017;139(9):091005.
DOI: https://doi.org/10.65731/ama/2026-0056 | Journal eISSN: 2300-5319 | Journal ISSN: 1898-4088
Language: English
Page range: 563 - 576
Submitted on: Dec 26, 2025
Accepted on: May 21, 2026
Published online: Sep 5, 2026
Published in print: Sep 1, 2026
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services

© 2026 Vinoth Kumar Harikrishnan, Sathiyamoorthy Margabandu, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.