Additive manufacturing (AM), one of the emerging disruptive technologies, is gaining popularity not only in rapid prototyping but also in manufacturing of complex shapes and dimensions. Artificial intelligence (AI) is the intelligence exhibited by computer systems to perform complex tasks such as learning, reasoning, decision making and problem solving. Machine learning (ML) is a subset of artificial intelligence which enables AI to imitate human learning process by using data and algorithms. The concept of machine intelligence which helps the advanced computing technologies to interact with the environment and highlights the intersection of AI and ML. The aim of this review article is to provide comprehensive information about the application of AI and ML in various additive manufacturing processes for different activities in order to improve the performance of the operation. Also, it describes the application of other advanced technologies such as Internet of Things (IoT), Digital Twins (DT) and Block Chain Technology to augment the additive manufacturing in producing quality products. Further, the article explains the various challenges that are encountered and the certain areas need to be addressed in future for the enhancement of quality product production by the application
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Valentina De Simone, Valentina Di Pasquale, & Salvatore Miranda. (2023). An overview on the use of AI/ML in manufacturing MSMEs: Solved issues, limits, and challenges. Procedia Computer Science, 217, 1820-1829. https://doi.org/10.1016/j.procs.2022.12.382
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Sofia, J., Sivabalan, T., Ethiraj, N., & Nikolova, M.P. (2021). A review of additive manufacturing for synthetic bone grafts and dental implants. Journal of Manufacturing Technology Research, 13(1-2), 29-52.
Christian F. Durach, Stefan Kurpjuweit, & Stephan M. Wagner. (2017). The impact of additive manufacturing on supply chains. International Journal of Physical Distribution & Logistics Management, 47(10), 954 – 971. https://doi.org/10.1108/IJPDLM-11-2016-0332
Rishi Parvanda, Prateek Kala, & Varun Sharma. (2024). Bibliometric analysis-based review of fused deposition modeling 3D printing method (1994–2020). 3D Printing and Additive Manufacturing, 11(1), 383 – 405. https://doi.org/10.1089/3dp.2021.0046
Khadija Meghraoui, Imane Sebari, Saloua Bensiali, & Kenza Ait El Kadi. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118 – 126.
Hüseyin Firat Kayiran. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1(2), 99 – 107.
[Danjuma Maza, Joshua Olufemi Ojo, & Grace Olubumi Akinlade. (2024). A predictive machine learning framework for diabetes. Turkish Journal of Engineering, 8(3), 583 – 592.
https://doi.org/10.31127/tuje.1434305
Mikhael Sayat, Rungkaew Sammavuthichai, Harini Shanika Wijeratne, Sarinya Jitklongsub, Priyanka Ghatole, & Bernard Isaiah Lo. (2022). Quantum technology, artificial intelligence, machine learning, and additive manufacturing in the Asia-Pacific for Mars exploration. Proceedings of the 73rd International Astronautical Congress, 18-22 September, Paris, France, Paper ID 70015.
Anbesh Jamwal, Rajeev Agrawal, & Monica Sharma. (2022). Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications. International Journal of Information Management Data Insights.2,100107. https://doi.org/10.1016/j.jjimei.2022.100107
Simon Fahle, Christopher Prinz, & Bernd Kuhlenkötter. (2020). Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application. Procedia CIRP, 93, 413 – 418. https://doi.org/10.1016/j.procir.2020.04.109
Wang Yuan Bin, Zheng Pai, Peng Tao, Yang HuaYong, & Zou Jun. (2020). Smart additive manufacturing: Current artificial intelligence enabled methods and future perspectives. Science China Technological Sciences, 63, 1600 – 1611. https://doi.org/10.1007/s11431-020-1581-2
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Won-Jung Oh, Choon-Man Lee, & Dong-Hyeon Kim. (2022). Prediction of deposition bead geometry in wire arc additive manufacturing using machine learning. Journal of Materials Research and Technology, 20,4283 – 4296. https://doi.org/10.1016/j.jmrt.2022.08.154
Dong-Ook Kim, Choon-Man Lee, & Dong-Hyeon Kim. (2024). Determining optimal bead central angle by applying machine learning to wire arc additive manufacturing (WAAM). Heliyon, 10, e23372. https://doi.org/10.1016/j.heliyon.2023.e23372
Jan Petrik, Benjamin Sydow, & Markus Bambach. (2022). Beyond parabolic weld bead models: AI-based 3D reconstruction of weld beads under transient conditions in wire-arc additive manufacturing, Journal of Materials Processing Technology, 302,117457.
https://doi.org/10.1016/j.jmatprotec.2021.117457
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Hyunwoong Ko, Paul Witherell, Yan Lu, Samyeon Kim, & David W. Rosen. (2021). Machine learning and knowledge graph based design rule construction for additive manufacturing. Additive Manufacturing, 37, 101620. https://doi.org/10.1016/j.addma.2020.101620
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Soori, M., Arezoo, B., & Dastres, R. (2023). Virtual manufacturing in industry 4.0: A review. Data Science and Management, 7(1), 47–63. https://doi.org/10.1016/j.dsm.2023.10.006
Valentina De Simone, Valentina Di Pasquale, & Salvatore Miranda. (2023). An overview on the use of AI/ML in manufacturing MSMEs: Solved issues, limits, and challenges. Procedia Computer Science, 217, 1820-1829. https://doi.org/10.1016/j.procs.2022.12.382
Wei Gao, Yunbo Zhang, Devarajan Ramanujan, Karthik Ramani, Yong Chen, Christopher B.Williams, Charlie C.L.Wang, Yuan C. Shin, Song Zhang, & Pablo D. Zayattieri. (2015). The status, challenges, and future of additive manufacturing in engineering. Computer-Aided Design,69,65-89. http://dx.doi.org/10.1016/j.cad.2015.04.001
Selin Yalçın. (2024) IVPF-AHP integrated VIKOR methodology in supplier selection of three-dimensional (3D) printers. Turkish Journal of Engineering, 8(2), 235 – 253. https://doi.org/10.31127/tuje.1404694
Sofia, J., Sivabalan, T., Ethiraj, N., & Nikolova, M.P. (2021). A review of additive manufacturing for synthetic bone grafts and dental implants. Journal of Manufacturing Technology Research, 13(1-2), 29-52.
Christian F. Durach, Stefan Kurpjuweit, & Stephan M. Wagner. (2017). The impact of additive manufacturing on supply chains. International Journal of Physical Distribution & Logistics Management, 47(10), 954 – 971. https://doi.org/10.1108/IJPDLM-11-2016-0332
Rishi Parvanda, Prateek Kala, & Varun Sharma. (2024). Bibliometric analysis-based review of fused deposition modeling 3D printing method (1994–2020). 3D Printing and Additive Manufacturing, 11(1), 383 – 405. https://doi.org/10.1089/3dp.2021.0046
Khadija Meghraoui, Imane Sebari, Saloua Bensiali, & Kenza Ait El Kadi. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118 – 126.
Hüseyin Firat Kayiran. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1(2), 99 – 107.
[Danjuma Maza, Joshua Olufemi Ojo, & Grace Olubumi Akinlade. (2024). A predictive machine learning framework for diabetes. Turkish Journal of Engineering, 8(3), 583 – 592.
https://doi.org/10.31127/tuje.1434305
Mikhael Sayat, Rungkaew Sammavuthichai, Harini Shanika Wijeratne, Sarinya Jitklongsub, Priyanka Ghatole, & Bernard Isaiah Lo. (2022). Quantum technology, artificial intelligence, machine learning, and additive manufacturing in the Asia-Pacific for Mars exploration. Proceedings of the 73rd International Astronautical Congress, 18-22 September, Paris, France, Paper ID 70015.
Anbesh Jamwal, Rajeev Agrawal, & Monica Sharma. (2022). Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications. International Journal of Information Management Data Insights.2,100107. https://doi.org/10.1016/j.jjimei.2022.100107
Simon Fahle, Christopher Prinz, & Bernd Kuhlenkötter. (2020). Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application. Procedia CIRP, 93, 413 – 418. https://doi.org/10.1016/j.procir.2020.04.109
Wang Yuan Bin, Zheng Pai, Peng Tao, Yang HuaYong, & Zou Jun. (2020). Smart additive manufacturing: Current artificial intelligence enabled methods and future perspectives. Science China Technological Sciences, 63, 1600 – 1611. https://doi.org/10.1007/s11431-020-1581-2
Sen Liu, Aaron P. Stebner, Branden B. Kappes, & Xiaoli Zhang. (2021). Machine learning for knowledge transfer across multiple metals additive manufacturing printers. Additive Manufacturing, 39, 101877. https://doi.org/10.1016/j.addma.2021.101877
Xiaoyu Li, Mengna Zhang, Mingxia Zhou, Jing Wang, Weixin Zhu, Chuan Wu, & Xiao Zhang. (2023). Qualify assessment for extrusion-based additive manufacturing with 3D scan and machine learning, Journal of Manufacturing Processes, 90, 274 – 285. https://doi.org/10.1016/j.jmapro.2023.01.025
Zeqing Jin, Zhizhou Zhang, & Grace X. Gu. (2019). Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning. Manufacturing Letters, 22, 11-15. https://doi.org/10.1016/j.mfglet.2019.09.005
Weizhe Tian, Qingya Li, Qihan Wang, Da Chen, & Wei Gao. (2024). Additive manufacturing error quantification on stability of composite sandwich plates with lattice-cores through machine learning technique, Composite Structures,327,117645. https://doi.org/10.1016/j.compstruct.2023.117645
Won-Jung Oh, Choon-Man Lee, & Dong-Hyeon Kim. (2022). Prediction of deposition bead geometry in wire arc additive manufacturing using machine learning. Journal of Materials Research and Technology, 20,4283 – 4296. https://doi.org/10.1016/j.jmrt.2022.08.154
Dong-Ook Kim, Choon-Man Lee, & Dong-Hyeon Kim. (2024). Determining optimal bead central angle by applying machine learning to wire arc additive manufacturing (WAAM). Heliyon, 10, e23372. https://doi.org/10.1016/j.heliyon.2023.e23372
Jan Petrik, Benjamin Sydow, & Markus Bambach. (2022). Beyond parabolic weld bead models: AI-based 3D reconstruction of weld beads under transient conditions in wire-arc additive manufacturing, Journal of Materials Processing Technology, 302,117457.
https://doi.org/10.1016/j.jmatprotec.2021.117457
Parand Akbari, Francis Ogoke, Ning-Yu Kao, Kazem Meidani, Chun-Yu Yeh, William Lee, & Amir Barati Farimani. (2022). MeltpoolNet: Melt pool characteristic prediction in metal additive manufacturing using machine learning. Additive Manufacturing, 55, 102817. https://doi.org/10.1016/j.addma.2022.102817
Steven Malley, Crystal Reina, Somer Nacy, Jérôme Gilles, Behrad Koohbor, & George Youssef. (2022). Predictability of mechanical behavior of additively manufactured particulate composites using machine learning and data-driven approaches. Computers in Industry, 142, 103739. https://doi.org/10.1016/j.compind.2022.103739
Shafaq Zia, Johan E. Carlson, & Pia Åkerfeldt. (2024). Prediction of manufacturing parameters of additively manufactured 316L steel samples using ultrasound fingerprinting, Ultrasonics, 137, 107196. https://doi.org/10.1016/j.ultras.2023.107196
Wasmer, K., Drissi-daoudi, R., Masinelli, G., Quang-le, T., Loge, R., & Shevchik, S. (2023). When AM (Additive Manufacturing) meets AE (Acoustic Emission) and AI (Artificial Intelligence). EWGAE35 & ICAE10 Conference on Acoustic Emission Testing, Ljubljana, Slovenia, September 2022, e-Journal of Nondestructive Testing, 28(1). https://doi.org/10.58286/27606
Jan Zenisek, Holger Gröning, Norbert Wild, Aziz Huskic, & Michael Affenzeller. (2022). Machine learning based data stream merging in additive manufacturing. Procedia Computer Science, 200, 1422 – 1431. https://doi.org/10.1016/j.procs.2022.01.343
Stylianos Vagenas, & George Panoutsos. (2023). Stability in reinforcement learning process control for additive manufacturing. IFAC Papers online, 56(2), 4719 - 4724. https://doi.org/10.1016/j.ifacol.2023.10.1233
Hyunwoong Ko, Paul Witherell, Yan Lu, Samyeon Kim, & David W. Rosen. (2021). Machine learning and knowledge graph based design rule construction for additive manufacturing. Additive Manufacturing, 37, 101620. https://doi.org/10.1016/j.addma.2020.101620
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Ethiraj, N., Sivabalan, T., Sofia, J., Harika, D., et al. (2025). A comprehensive review on application of machine intelligence in additive manufacturing. Turkish Journal of Engineering, 9(1), 37-46. https://doi.org/10.31127/tuje.1502587
AMA
Ethiraj N, Sivabalan T, Sofia J, Harika D, Nikolova M. A comprehensive review on application of machine intelligence in additive manufacturing. TUJE. January 2025;9(1):37-46. doi:10.31127/tuje.1502587
Chicago
Ethiraj, N, T Sivabalan, J Sofia, Dommaraju Harika, and M.p Nikolova. “A Comprehensive Review on Application of Machine Intelligence in Additive Manufacturing”. Turkish Journal of Engineering 9, no. 1 (January 2025): 37-46. https://doi.org/10.31127/tuje.1502587.
EndNote
Ethiraj N, Sivabalan T, Sofia J, Harika D, Nikolova M (January 1, 2025) A comprehensive review on application of machine intelligence in additive manufacturing. Turkish Journal of Engineering 9 1 37–46.
IEEE
N. Ethiraj, T. Sivabalan, J. Sofia, D. Harika, and M. Nikolova, “A comprehensive review on application of machine intelligence in additive manufacturing”, TUJE, vol. 9, no. 1, pp. 37–46, 2025, doi: 10.31127/tuje.1502587.
ISNAD
Ethiraj, N et al. “A Comprehensive Review on Application of Machine Intelligence in Additive Manufacturing”. Turkish Journal of Engineering 9/1 (January 2025), 37-46. https://doi.org/10.31127/tuje.1502587.
JAMA
Ethiraj N, Sivabalan T, Sofia J, Harika D, Nikolova M. A comprehensive review on application of machine intelligence in additive manufacturing. TUJE. 2025;9:37–46.
MLA
Ethiraj, N et al. “A Comprehensive Review on Application of Machine Intelligence in Additive Manufacturing”. Turkish Journal of Engineering, vol. 9, no. 1, 2025, pp. 37-46, doi:10.31127/tuje.1502587.
Vancouver
Ethiraj N, Sivabalan T, Sofia J, Harika D, Nikolova M. A comprehensive review on application of machine intelligence in additive manufacturing. TUJE. 2025;9(1):37-46.