Skip to main content
SpringerLink
Log in

Application of artificial intelligence in additive manufacturing

  • Review
  • Published:
JMST Advances Aims and scope Submit manuscript

Abstract

Over the past decade, additive manufacturing (AM) technology has made significant strides and found diverse applications across sectors like healthcare, aerospace, and construction, contributing to its growth. Various methodologies have been devised to support and enhance the utilization of AM technology. Notably, artificial intelligence (AI) has played a pivotal role in analyzing and supporting the intricate physical phenomena associated with AM. AI’s application in AM can be categorized into four key domains. Firstly, AI streamlines design processes by considering AM-specific parameters, promoting innovation. Secondly, it facilitates material development, creating customized materials for AM. Thirdly, AI optimizes AM processes through real-time control, improving process selection and execution. Lastly, AI ensures quality through predictive models and real-time monitoring. This paper offers an overview of AI techniques applied in the realm of AM technology, focusing on these four perspectives. It demonstrates how AI enhances design efficiency, aids in material development, optimizes AM processes, and guarantees the quality of AM-produced outputs. Additionally, the paper outlines research directions for effectively harnessing AI’s potential within the AM field.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. K. Park, Design for additive manufacturing (DfAM). J. KSME 60(11), 29–32 (2020)

    Google Scholar 

  2. J. Jiang, Y. Xiong, Z. Zhang, D.W. Rosen, Machine learning integrated design for additive manufacturing. J. Intell. Manuf. 33(4), 1073–1086 (2022)

    Article  Google Scholar 

  3. G.D. Goh, S.L. Sing, W.Y. Yeong, A review on machine learning in 3D printing: applications, potential, and challenges. Artif. Intell. Rev. 54(1), 63–94 (2021)

    Article  Google Scholar 

  4. Z. Zhu, N. Anwer, Q. Huang, L. Mathieu, Machine learning in tolerancing for additive manufacturing. CIRP Ann. 67(1), 157–160 (2018)

    Article  Google Scholar 

  5. Z. Zhu, K. Ferreira, N. Anwer, L. Mathieu, K. Guo, L. Qiao, Convolutional neural network for geometric deviation prediction in additive manufacturing. Procedia Cirp 91, 534–539 (2020)

    Article  Google Scholar 

  6. M. Khanzadeh, P. Rao, R. Jafari-Marandi, B.K. Smith, M.A. Tschopp, L. Bian, Quantifying geometric accuracy with unsupervised machine learning: using self-organizing map on fused filament fabrication additive manufacturing parts. J. Manuf. Sci. Eng. 140(3), 031011 (2018)

    Article  Google Scholar 

  7. R. Li, M. Jin, V.C. Paquit, Geometrical defect detection for additive manufacturing with machine learning models. Mater. Des. 206, 109726 (2021)

    Article  Google Scholar 

  8. N. Decker, M. Lyu, Y. Wang, Q. Huang, Geometric accuracy prediction and improvement for additive manufacturing using triangular mesh shape data. J. Manuf. Sci. Eng. 143(6), 061006 (2021)

    Article  Google Scholar 

  9. S.L. Chan, Y. Lu, Y. Wang, Data-driven cost estimation for additive manufacturing in cybermanufacturing. J. Manuf. Syst. 46, 115–126 (2018)

    Article  Google Scholar 

  10. Y. Oh, M. Sharp, T. Sprock, S. Kwon, Neural network-based build time estimation for additive manufacturing: a performance comparison. J. Comput. Des. Eng. 8(5), 1243–1256 (2021)

    Google Scholar 

  11. A.J. Lew, M.J. Buehler, Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model. Forces Mech. 5, 100054 (2021)

    Article  Google Scholar 

  12. S. Oh, Y. Jung, S. Kim, I. Lee, N. Kang, Deep generative design: integration of topology optimization and generative models. J. Mech. Des. 141(11), 111405 (2019)

    Article  Google Scholar 

  13. S. Chinchanikar, A.A. Shaikh, A review on machine learning, big data analytics, and design for additive manufacturing for aerospace applications. J. Mater. Eng. Perform. 31(8), 6112–6130 (2022)

    Article  Google Scholar 

  14. H. Moon, D.J. McGregor, N. Miljkovic, W.P. King, Ultra-power-dense heat exchanger development through genetic algorithm design and additive manufacturing. Joule 5(11), 3045–3056 (2021)

    Article  Google Scholar 

  15. L. Han, W. Du, Z. Xia, B. Gao, M. Yang, Generative design and integrated 3D printing manufacture of cross joints. Materials 15(14), 4753 (2022)

    Article  Google Scholar 

  16. E.A. Castañeda, A.D. Asmat, M.J. Pejerrey, C.M. Jara, L.G. Cabrejos, J. Cornejo. Generative design and DEM-FEA simulations for optimization and validation of a bio-inspired airless tire-wheel system for land-based space planetary exploration robot, in 2022 International Conference on Advanced Robotics and Mechatronics (ICARM). (IEEE, 2022)

  17. B.R. Jerrin, S. Suryaprakash, A. Giridharan, Optimization of quadcopter frame using generative design and comparison with DJI F450 drone frame. IOP. Conf. Ser. Mater. Sci. Eng. 1012(1), 12019 (2021)

    Article  Google Scholar 

  18. M. Pollák, M. Töröková, M. Kočiško, Utilization of generative design tools in designing components necessary for 3D printing done by a robot. TEM J. 9(3), 868 (2020)

    Article  Google Scholar 

  19. F. De Crescenzio, M. Fantini, E. Asllani, Generative design of 3D printed hands-free door handles for reduction of contagion risk in public buildings. Int. J. Interact. Des. Manuf. (IJIDeM) 16(1), 253–261 (2022)

    Article  Google Scholar 

  20. Y. Zhang, Z. Wang, Y. Zhang, S. Gomes, A. Bernard, Bio-inspired generative design for support structure generation and optimization in additive manufacturing (AM). CIRP Ann. Manuf. Technol. 69(1), 117–120 (2020)

    Article  Google Scholar 

  21. T. Briard, F. Segonds, N. Zamariola, G-DfAM: a methodological proposal of generative design for additive manufacturing in the automotive industry. Int. J. Interact. Des. Manuf. (IJIDeM) 14(3), 875–886 (2020)

    Article  Google Scholar 

  22. S. Dhurjad, A. Shaikh, S. Chinchanikar, Generative Design for Additive Manufacturing (G-DFAM): An Explorative Study of Aerospace Brackets, in AIP Conference Proceedings. (AIP Publishing, 2023)

    Google Scholar 

  23. Z. Wang, Y. Zhang, A. Bernard, A constructive solid geometry-based generative design method for additive manufacturing. Addit. Manuf. 41, 101952 (2021)

    Google Scholar 

  24. B. Duan, Analysis on the value of 3D printing in jewelry design based on artificial intelligence. J. Phys. Conf. Ser. (2021). https://doi.org/10.1088/1742-6596/1744/4/042132

    Article  Google Scholar 

  25. X. Yao, S.K. Moon, G. Bi, A hybrid machine learning approach for additive manufacturing design feature recommendation. Rapid Prototyp. J. 23(6), 983–997 (2017)

    Article  Google Scholar 

  26. D. Shu, J. Cunningham, G. Stump, S.W. Miller, M.A. Yukish, T.W. Simpson, C.S. Tucker, 3d design using generative adversarial networks and physics-based validation. J. Mech. Des. 142(7), 071701 (2020)

    Article  Google Scholar 

  27. S. Kim, D.W. Rosen, P. Witherell, H. Ko, A design for additive manufacturing ontology to support manufacturability analysis. J. Comput. Inf. Sci. Eng. 19(4), 041014 (2019)

    Article  Google Scholar 

  28. H. Ko, P. Witherell, Y. Lu, S. Kim, D.W. Rosen, Machine learning and knowledge graph based design rule construction for additive manufacturing. Addit. Manuf. 37, 101620 (2021)

    Google Scholar 

  29. J. Ahn, J. Doh, S. Kim, S.-I. Park, Knowledge-based design algorithm for support reduction in material extrusion additive manufacturing. Micromachines 13(10), 1672 (2022)

    Article  Google Scholar 

  30. G. Formentini, C. Favi, M. Mandolini, M. Germani, A framework to collect and reuse engineering knowledge in the context of design for additive manufacturing. Proc. Des. Soc. 2, 1371–1380 (2022)

    Article  Google Scholar 

  31. P. Schaechtl, S. Goetz, B. Schleich, S. Wartzack, Knowledge-driven design for additive manufacturing: a framework for design adaptation. Proc. Des. Soc. 3, 2405–2414 (2023)

    Article  Google Scholar 

  32. G. Williams, N.A. Meisel, T.W. Simpson, C. McComb, Design repository effectiveness for 3D convolutional neural networks: application to additive manufacturing. J. Mech. Des. 141(11), 111701 (2019)

    Article  Google Scholar 

  33. Y. Tang, G. Dong, Y. Xiong, Q. Wang, Data-driven design of customized porous lattice sole fabricated by additive manufacturing. Procedia Manuf. 53, 318–326 (2021)

    Article  Google Scholar 

  34. A. Koeppe, C.A.H. Padilla, M. Voshage, J.H. Schleifenbaum, B. Markert, Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks. Manuf. Lett. 15, 147–150 (2018)

    Article  Google Scholar 

  35. N. Despres, E. Cyr, P. Setoodeh, M. Mohammadi, Deep learning and design for additive manufacturing: a framework for microlattice architecture. Jom 72, 2408–2418 (2020)

    Article  Google Scholar 

  36. J.D. Alejandrino, R.S. Concepcion II., S.C. Lauguico, R.R. Tobias, L. Venancio, D. Macasaet, A.A. Bandala, E.P. Dadios, A machine learning approach of lattice infill pattern for increasing material efficiency in additive manufacturing processes. Int. J. Mech. Eng. Robot. Res 9(9), 1253–1263 (2020)

    Article  Google Scholar 

  37. G.X. Gu, C.-T. Chen, M.J. Buehler, De novo composite design based on machine learning algorithm. Extreme Mech. Lett. 18, 19–28 (2018)

    Article  Google Scholar 

  38. W. Sha, Y. Guo, Q. Yuan, S. Tang, X. Zhang, S. Lu, X. Guo, Y.-C. Cao, S. Cheng, Artificial intelligence to power the future of materials science and engineering. Adv. Intell. Syst. 2(4), 1900143 (2020)

    Article  Google Scholar 

  39. G. Dong, Y. Tang, D. Li, Y.F. Zhao, Design and optimization of solid lattice hybrid structures fabricated by additive manufacturing. Addit. Manuf. 33, 101116 (2020)

    Google Scholar 

  40. C. Wang, X. Tan, S.B. Tor, C. Lim, Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit. Manuf. 36, 101538 (2020)

    Google Scholar 

  41. C.-T. Chen, G.X. Gu, Machine learning for composite materials. MRs Commun. 9(2), 556–566 (2019)

    Article  Google Scholar 

  42. G. Xie, K. Wang, X. Wu, J. Wang, T. Li, Y. Peng, H. Zhang, A hybrid multi-stage decision-making method with probabilistic interval-valued hesitant fuzzy set for 3D printed composite material selection. Eng. Appl. Artif. Intell. 123, 106483 (2023)

    Article  Google Scholar 

  43. J. Qin, F. Hu, Y. Liu, P. Witherell, C.C. Wang, D.W. Rosen, T.W. Simpson, Y. Lu, Q. Tang, Research and application of machine learning for additive manufacturing. Addit. Manuf. 52, 102691 (2022)

    Google Scholar 

  44. L. Wang, Y.-C. Chan, F. Ahmed, Z. Liu, P. Zhu, W. Chen, Deep generative modeling for mechanistic-based learning and design of metamaterial systems. Comput. Methods Appl. Mech. Eng. 372, 113377 (2020)

    Article  MathSciNet  Google Scholar 

  45. C. Wen, Y. Zhang, C. Wang, D. Xue, Y. Bai, S. Antonov, L. Dai, T. Lookman, Y. Su, Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 170, 109–117 (2019)

    Article  Google Scholar 

  46. P.A. Rometsch, Y. Zhu, X. Wu, A. Huang, Review of high-strength aluminium alloys for additive manufacturing by laser powder bed fusion. Mater. Des. 219, 110779 (2022)

    Article  Google Scholar 

  47. N.G. Mbodj, M. Abuabiah, P. Plapper, M. El Kandaoui, S. Yaacoubi, Bead geometry prediction in laser-wire additive manufacturing process using machine learning: case of study. Appl. Sci. 11(24), 11949 (2021)

    Article  Google Scholar 

  48. F. Caiazzo, A. Caggiano, Laser direct metal deposition of 2024 Al alloy: trace geometry prediction via machine learning. Materials 11(3), 444 (2018)

    Article  Google Scholar 

  49. K. Aoyagi, H. Wang, H. Sudo, A. Chiba, Simple method to construct process maps for additive manufacturing using a support vector machine. Addit. Manuf. 27, 353–362 (2019)

    Google Scholar 

  50. R. Onler, A.S. Koca, B. Kirim, E. Soylemez, Multi-objective optimization of binder jet additive manufacturing of Co-Cr-Mo using machine learning. Int. J. Adv. Manuf. Technol. 119(1), 1091–1108 (2022)

    Article  Google Scholar 

  51. A.K. Sood, R.K. Ohdar, S.S. Mahapatra, Experimental investigation and empirical modelling of FDM process for compressive strength improvement. J. Adv. Res. 3(1), 81–90 (2012)

    Article  Google Scholar 

  52. A.K. Sood, A. Equbal, V. Toppo, R.K. Ohdar, S.S. Mahapatra, An investigation on sliding wear of FDM built parts. CIRP J. Manuf. Sci. Technol. 5(1), 48–54 (2012)

    Article  Google Scholar 

  53. P. Charalampous, N. Kladovasilakis, I. Kostavelis, K. Tsongas, D. Tzetzis, D. Tzovaras, Machine learning-based mechanical behavior optimization of 3D print constructs manufactured via the FFF process. J. Mater. Eng. Perform. 31(6), 4697–4706 (2022)

    Article  Google Scholar 

  54. T. Chepiga, P. Zhilyaev, A. Ryabov, A.P. Simonov, O.N. Dubinin, D.G. Firsov, Y.O. Kuzminova, S.A. Evlashin, Process parameter selection for production of stainless steel 316L using efficient multi-objective Bayesian optimization algorithm. Materials 16(3), 1050 (2023)

    Article  Google Scholar 

  55. L. Nguyen, J. Buhl, M. Bambach, Continuous Eulerian tool path strategies for wire-arc additive manufacturing of rib-web structures with machine-learning-based adaptive void filling. Addit. Manuf. 35, 101265 (2020)

    Google Scholar 

  56. D.S. Shin, C.H. Lee, U. Kühn, S.C. Lee, S.J. Park, H. Schwab, S. Scudino, K. Kosiba, Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence. J. Alloy. Compd. 862, 158018 (2021)

    Article  Google Scholar 

  57. A. Suzuki, Y. Shiba, H. Ibe, N. Takata, M. Kobashi, Machine-learning assisted optimization of process parameters for controlling the microstructure in a laser powder bed fused WC/Co cemented carbide. Addit. Manuf. 59, 103089 (2022)

    Google Scholar 

  58. A. Costa, G. Buffa, D. Palmeri, G. Pollara, L. Fratini, Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy. J. Intell. Manuf. 33(7), 1967–1989 (2022)

    Article  Google Scholar 

  59. S. Lapointe, G. Guss, Z. Reese, M. Strantza, M.J. Matthews, C.L. Druzgalski, Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries. Addit. Manuf. 53, 102687 (2022)

    Google Scholar 

  60. C. Silbernagel, A. Aremu, I. Ashcroft, Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing. Rapid Prototyp. J. 26(4), 625–637 (2020)

    Article  Google Scholar 

  61. N. Jyeniskhan, A. Keutayeva, G. Kazbek, M.H. Ali, E. Shehab, Integrating machine learning model and digital twin system for additive manufacturing. IEEE Access 11, 71113–71126 (2023)

    Article  Google Scholar 

  62. L. Lu, J. Hou, S.Q. Yuan, X.L. Yao, Y.M. Li, J.H. Zhu, Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites. Robot. Comput.-Integr. Manuf. 79, 102431 (2023)

    Article  Google Scholar 

  63. A. Balu, S. Sarkar, B. Ganapathysubramanian, A. Krishnamurthy, Physics-aware machine learning surrogates for real-time manufacturing digital twin. Manuf. Lett. 34, 71–74 (2022)

    Article  Google Scholar 

  64. T.S. Tamir, G. Xiong, Q.H. Fang, Y. Yang, Z. Shen, M.C. Zhou, J.C. Jiang, Machine-learning-based monitoring and optimization of processing parameters in 3D printing. Int. J. Comput. Integr. Manuf. (2022). https://doi.org/10.1080/0951192X.2022.2145019

    Article  Google Scholar 

  65. Z.L. Zhang, Z.T. Yang, R.D. Sisson, J.Y. Liang, Improving ceramic additive manufacturing via machine learning-enabled closed-loop control. Int. J. Appl. Ceram. Technol. 19(2), 957–967 (2022)

    Article  Google Scholar 

  66. D.A.J. Brion, S.W. Pattinson, Quantitative and real-time control of 3D printing material flow through deep learning. Adv. Intell. Syst. (2022). https://doi.org/10.1002/aisy.202200153

    Article  Google Scholar 

  67. R.-J. Wang, J. Li, F. Wang, X. Li, Q. Wu, ANN model for the prediction of density in selective laser sintering. Int. J. Manuf. Res. 4(3), 362–373 (2009)

    Article  Google Scholar 

  68. X. Shen, J. Yao, Y. Wang, J. Yang. Density prediction of selective laser sintering parts based on artificial neural network. in Advances in Neural Networks-ISNN 2004: International Symposium on Neural Networks, Dalian, China, August 19–21, 2004, Proceedings, Part II 1. (Springer, 2004)

  69. A. Equbal, A.K. Sood, S.S. Mahapatra, Prediction of dimensional accuracy in fused deposition modelling: a fuzzy logic approach. Int. J. Prod. Qual. Manag. 7(1), 22–43 (2011)

    Google Scholar 

  70. A. Yaseer, H. Chen, Machine learning based layer roughness modeling in robotic additive manufacturing. J. Manuf. Process. 70, 543–552 (2021)

    Article  Google Scholar 

  71. M. Chandra, K.E.K. Vimal, S. Rajak, A comparative study of machine learning algorithms in the prediction of bead geometry in wire-arc additive manufacturing. Int. J. Interact. Des. Manuf. (IJIDeM) (2023). https://doi.org/10.1007/s12008-023-01326-4

    Article  Google Scholar 

  72. T. Wang, T.-H. Kwok, C. Zhou, S. Vader, In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. J. Manuf. Syst. 47, 83–92 (2018)

    Article  Google Scholar 

  73. Y. Zhang, G.S. Hong, D. Ye, K. Zhu, J.Y.H. Fuh, Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Mater. Des. 156, 458–469 (2018)

    Article  Google Scholar 

  74. Z.Y. Zhang, Z.C. Liu, D.Z. Wu, Prediction of melt pool temperature in directed energy deposition using machine learning. Addit. Manuf. 37, 101692 (2021)

    Google Scholar 

  75. H. Baumgartl, J. Tomas, R. Buettner, M. Merkel, A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Prog. Addit. Manuf. 5(3), 277–285 (2020)

    Article  Google Scholar 

  76. B. Zhang, S.Y. Liu, Y.C. Shin, In-process monitoring of porosity during laser additive manufacturing process. Addit. Manuf. 28, 497–505 (2019)

    Google Scholar 

  77. A. Caggiano, J. Zhang, V. Alfieri, F. Caiazzo, R. Gao, R. Teti, Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Ann. 68(1), 451–454 (2019)

    Article  Google Scholar 

  78. L. Chen, X. Yao, C. Tan, W. He, J. Su, F. Weng, Y. Chew, N.P.H. Ng, S.K. Moon, In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning. Addit. Manuf. 69, 103547 (2023)

    Google Scholar 

  79. S.M. Estalaki, C.S. Lough, R.G. Landers, E.C. Kinzel, T. Luo, Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning. Addit. Manuf. 58, 103008 (2022)

    Google Scholar 

  80. J. Francis, L. Bian, Deep learning for distortion prediction in laser-based additive manufacturing using big data. Manuf. Lett. 20, 10–14 (2019)

    Article  Google Scholar 

  81. M. Khanzadeh, S. Chowdhury, M. Marufuzzaman, M.A. Tschopp, L. Bian, Porosity prediction: supervised-learning of thermal history for direct laser deposition. J. Manuf. Syst. 47, 69–82 (2018)

    Article  Google Scholar 

  82. G. Masinelli, S.A. Shevchik, V. Pandiyan, T. Quang-Le, K. Wasmer, Artificial Intelligence for Monitoring and Control of Metal Additive Manufacturing, in Industrializing Additive Manufacturing. (Springer International Publishing, Cham, 2021)

    Google Scholar 

  83. L. Scime, J. Beuth, Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit. Manuf. 19, 114–126 (2018)

    Google Scholar 

  84. L. Scime, J. Beuth, A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Addit. Manuf. 24, 273–286 (2018)

    Google Scholar 

  85. S.A. Shevchik, C. Kenel, C. Leinenbach, K. Wasmer, Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Addit. Manuf. 21, 598–604 (2018)

    Google Scholar 

  86. Z. Smoqi, A. Gaikwad, B. Bevans, M.H. Kobir, J. Craig, A. Abul-Haj, A. Peralta, P. Rao, Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning. J. Mater. Process. Technol. 304, 117550 (2022)

    Article  Google Scholar 

  87. K. Wasmer, T. Le-Quang, B. Meylan, S.A. Shevchik, In situ quality monitoring in am using acoustic emission: a reinforcement learning approach. J. Mater. Eng. Perform. 28(2), 666–672 (2019)

    Article  Google Scholar 

  88. E. Westphal, H. Seitz, A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Addit. Manuf. 41, 101965 (2021)

    Google Scholar 

  89. D. Wu, Y. Wei, J. Terpenny, Predictive modelling of surface roughness in fused deposition modelling using data fusion. Int. J. Prod. Res. 57(12), 3992–4006 (2019)

    Article  Google Scholar 

  90. A.P. Garland, B.C. White, B.H. Jared, M. Heiden, E. Donahue, B.L. Boyce, Deep convolutional neural networks as a rapid screening tool for complex additively manufactured structures. Addit. Manuf. 35, 101217 (2020)

    Google Scholar 

  91. Y. Li, H. Yan, Y. Zhang. A deep learning method for material performance recognition in laser additive manufacturing. in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019

  92. A. Raj, C. Owen, B. Stegman, H. Abdel-Khalik, X.H. Zhang, J.W. Sutherland, Predicting mechanical properties from co-axial melt pool monitoring signals in laser powder bed fusion. J. Manuf. Process. 101, 181–194 (2023)

    Article  Google Scholar 

  93. G. Csurka, C. Dance, L. Fan, J. Willamowski, C. Bray. Visual categorization with bags of keypoints. In Workshop on statistical learning in computer vision, ECCV (Prague, 2004)

  94. L.P. Kaelbling, M.L. Littman, A.W. Moore, Reinforcement Learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Basic Science Research Programs through the National Research Foundation of Korea (NRF) funded by the Korean government. (NRF-2021R1F1A1063297, PI: Sang-in Park) This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2021R1I1A3044394, PI: Jaehyeok Doh).

Funding

This article is funded by NRF, 2021R1F1A1063297, Sang-In Park, 2021R1I1A3044394, Jaehyeok Doh.

Author information

Author notes

  1. Sungmo Gu and Minhyeok Choi are equally contributed as the first author.

Authors and Affiliations

  1. Department of Mechatronics Engineering, Incheon National University, Incheon, 22012, Republic of Korea

    Sungmo Gu, Hwijae Park & Sang-in Park

  2. School of Mechanical and Material Convergence Engineering, Gyeongsang National University, Jinju-si, Gyeongsangnam-do, 52725, Republic of Korea

    Minhyeok Choi, Sangjun Jeong & Jaehyeok Doh

Authors
  1. Sungmo Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minhyeok Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hwijae Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sangjun Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jaehyeok Doh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sang-in Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jaehyeok Doh or Sang-in Park.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gu, S., Choi, M., Park, H. et al. Application of artificial intelligence in additive manufacturing. JMST Adv. 5, 93–104 (2023). https://doi.org/10.1007/s42791-023-00057-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42791-023-00057-7

Keywords

Search

Navigation