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A comprehensive review on application of machine intelligence in additive manufacturing

Year 2025, , 37 - 46, 20.01.2025
https://doi.org/10.31127/tuje.1502587

Abstract

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

References

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Year 2025, , 37 - 46, 20.01.2025
https://doi.org/10.31127/tuje.1502587

Abstract

References

  • 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, C., Tan, X.P., Tor, S.B., & Lim, C.S. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. https://doi.org/10.1016/j.addma.2020.101538
  • 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
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There are 68 citations in total.

Details

Primary Language English
Subjects Production Technologies
Journal Section Articles
Authors

N Ethiraj 0000-0002-7174-5443

T Sivabalan 0000-0003-1683-7481

J Sofia 0000-0002-9565-7712

Dommaraju Harika 0009-0004-1778-6924

M.p Nikolova 0000-0002-0597-7799

Early Pub Date January 17, 2025
Publication Date January 20, 2025
Submission Date June 25, 2024
Acceptance Date October 21, 2024
Published in Issue Year 2025

Cite

APA 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.
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