Machine Learning for Additive Manufacturing: Comparison
Please note this is a comparison between Version 2 by Vicky Zhou and Version 1 by Dean Grierson.

Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

  • machine learning
  • supervised learning
  • unsupervised learning
  • reinforcement learning
  • additive manufacturing
  • design for additive manufacturing
  • additive manufacturing process
  • additive manufacturing monitoring
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  1. ISO/ASTM. ISO/ASTM52900-15, Standard Terminology for Additive Manufacturing—General Principles—Terminology. ASTM Int. 2015, 1, 1–9.
  2. Ahlers, D.; Wasserfall, F.; Hendrich, N.; Zhang, J. 3D printing of nonplanar layers for smooth surface generation. In Proceedings of the IEEE International Conference on Automation Science and Engineering, Vancouver, BC, Canada, 22–26 August 2019; pp. 1737–1743.
  3. Thompson, M.K.; Moroni, G.; Vaneker, T.; Fadel, G.; Campbell, R.I.; Gibson, I.; Bernard, A.; Schulz, J.; Graf, P.; Ahuja, B.; et al. Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints. CIRP Ann. Manuf. Technol. 2016, 65, 737–760.
  4. Gibson, I.; Rosen, D.; Stucker, B.; Khorasani, M. Additive Manufacturing Technologies, 3rd ed.; Springer: Berlin/Heidelberg, Germany, 2021; ISBN 978-3-030-56127-7.
  5. Dowling, L.; Kennedy, J.; O’Shaughnessy, S.; Trimble, D. A review of critical repeatability and reproducibility issues in powder bed fusion. Mater. Des. 2020, 186, 108346.
  6. Sutton, R.S.; Barto, A.G. Reinforcement Learning: An Introduction, 2nd ed.; The MIT Press: Cambridge, MA, USA, 2018; ISBN 9780262039246.
  7. Shinde, P.P.; Shah, S. A Review of Machine Learning and Deep Learning Applications. In Proceedings of the 2018 4th International Conference on Computing, Communication Control and Automation (ICCUBEA), Pune, India, 16–18 August 2018.
  8. Wang, C.; Tan, X.P.; Tor, S.B.; Lim, C.S. Machine learning in additive manufacturing: State-of-the-art and perspectives. Addit. Manuf. 2020, 36, 101538.
  9. 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, 721–729 101016/jeng201904012.
  10. Darwish, S.M.H.; Aslam, M.U. Auxetic cellular structures for custom made orthopedic implants using additive manufacturing. Int. J. Eng. Adv. Technol. 2014, 4, 2249–8958.
  11. Xie, G.; Dong, Y.; Zhou, J.; Sheng, Z. Topology optimization design of hydraulic valve blocks for additive manufacturing. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2020, 234, 1899–1912.
  12. Diegel, O.; Schutte, J.; Ferreira, A.; Chan, Y.L. Design for additive manufacturing process for a lightweight hydraulic manifold. Addit. Manuf. 2020, 36, 101446.
  13. GE Additive. For the Ready. Launch Your Additive Manufacturing Program with Help from GE Additive. 2020. Available online: (accessed on 16 June 2021).
  14. Redwood, B.; Schoffer, F.; Garret, B. The 3D Printing Handbook: Technologies, Design and Applications, 1st ed.; 3D Hubs: Amsterdam, The Netherlands, 2018; ISBN 9789082748505.
  15. Chen, R.K.; Jin, Y.-A.; Wensman, J.; Shih, A. Additive manufacturing of custom orthoses and prostheses-A review. Addit. Manuf. 2016, 12, 77–89.
  16. EOS Shoe Soles from the 3D Printer|EOS GmbH. Available online: (accessed on 28 May 2021).
  17. Dong, G.; Tessier, D.; Zhao, Y.F. Design of Shoe Soles Using Lattice Structures Fabricated by Additive Manufacturing. Proc. Des. Soc. Int. Conf. Eng. Des. 2019, 5–8.
  18. Liu, P.; Huang, S.H.; Mokasdar, A.; Zhou, H.; Hou, L. Production Planning & Control The Management of Operations The impact of additive manufacturing in the aircraft spare parts supply chain: Supply chain operation reference (scor) model based analysis. Prod. Plan. Control. 2014, 25, 1169–1181.
  19. Hernandez, B.R.; Housel, T.; Ford, D. An Investigation into the Use of £D Scanning and PRinting Technologies in the Navy Collaborative Product Lifecycle Management. Master’s Thesis, Naval Postgraduate SCHOOL, Monterey, CA, USA, 2013. Available online: (accessed on 16 June 2021).
  20. Hastie, T.; Tibshirani, R.; Friedman, J. Springer Series in Statistics The Elements of Statistical Learning Data Mining, Inference, and Prediction, 2nd ed.; Springer: Berlin/Heidelberg, Germany, 2008; ISBN 978-0-387-84857-0.
  21. Sosnovik, I.; Oseledets, I. Neural networks for topology optimization. Russ. J. Numer. Anal. Math. Model. 2019, 34, 215–223.
  22. Banga, S.; Gehani, H.; Bhilare, S.; Patel, J.; Kara, B. 3D Topology Optimzation Using Convolutional Neural Networks. arXiv 2018, arXiv:1808.07440.
  23. Harish, B.; Eswara Sai Kumar, K.; Srinivasan, B. Topology optimization using convolutional neural network. In Proceedings of the Lecture Notes in Mechanical Engineering; Springer: Berlin/Heidelberg, Germany, 2020; pp. 301–307.
  24. Gu, G.X.; Chun-Teh Chen, A.; Richmond, D.J.; Buehler, M.J. Bioinspired hierarchical composite design using machine learning: Simulation, additive manufacturing, and experiment. Mater. Horizons 2018, 5, 939.
  25. Jiang, J.; Xiong, Y.; Zhang, Z.; Rosen, D.W. Machine learning integrated design for additive manufacturing. J. Intell. Manuf. 2020, 1–14.
  26. Yao, X.; Moon, S.K.; Bi, G. A hybrid machine learning approach for additive manufacturing design feature recommendation. Rapid Prototyp. J. 2017, 23.
  27. Mahmood, M.A.; Visan, A.I.; Ristoscu, C.; Mihailescu, I.N. Artificial Neural Network Algorithms for 3D Printing. Materials 2021, 14, 163.
  28. Silbernagel, C.; Aremu, A.; Ashcroft, I. Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing. Rapid Prototyp. J. 2019, 26, 625–637.
  29. Kappes, B.; Moorthy, S.; Drake, D.; Geerlings, H.; Stebner, A. Machine learning to optimize additive manufacturing parameters for laser powder bed fusion of Inconel 718. In Proceedings of the Minerals, Metals and Materials Series; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; Volume 2018-June, pp. 595–627.
  30. Garg, A.; Lam, J.S.L. Measurement of environmental aspect of 3-D printing process using soft computing methods. Meas. J. Int. Meas. Confed. 2015, 75, 210–217.
  31. Garg, A.; Lam, J.S.L.; Savalani, M.M. A new computational intelligence approach in formulation of functional relationship of open porosity of the additive manufacturing process. Int. J. Adv. Manuf. Technol. 2015, 80, 555–565.
  32. 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, 1943–1958.
  33. Li, Z.; Zhang, Z.; Shi, J.; Wu, D. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robot. Comput. Integr. Manuf. 2019, 57, 488–495.
  34. Xiong, J.; Zhang, G.; Hu, J.; Wu, L. Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J. Intell. Manuf. 2014, 25, 157–163.
  35. Li, Y.; Sun, Y.; Han, Q.; Zhang, G.; Horváth, I. Enhanced beads overlapping model for wire and arc additive manufacturing of multi-layer multi-bead metallic parts. J. Mater. Process. Technol. 2018, 252, 838–848.
  36. Lu, Z.; Li, Ã.; Lu, B.; Zhang, A.; Zhu, G.; Pi, G. The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks ARTICLE IN PRESS. Opt. Lasers Eng. 2010, 48, 519–525.
  37. Caiazzo, F.; Caggiano, A. Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning. Materials 2018, 11, 444.
  38. Mozaffar, M.; Paul, A.; Al-Bahrani, R.; Wolff, S.; Choudhary, A.; Agrawal, A.; Ehmann, K.; Cao, J. Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manuf. Lett. 2018, 18, 35–39.
  39. Narayana, P.L.; Kim, J.H.; Lee, J.; Choi, S.-W.; Lee, S.; Park, C.H.; Yeom, J.-T.; Reddy, N.G.S.; Hong, J.-K. Optimization of process parameters for direct energy deposited Ti-6Al-4V alloy using neural networks. Int. J. Adv. Manuf. Technol. 2021, 114, 3269–3283.
  40. Xia, C.; Pan, Z.; Polden, J.; Li, H.; Xu, Y.; Chen, S. Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. J. Intell. Manuf. 2021, 1–16.
  41. Chen, H.; Zhao, Y.F. Learning Algorithm Based Modeling and Process Parameters Recommendation System for Binder Jetting Additive Manufacturing Process. Proc. ASME Des. Eng. Tech. Conf. 2016, 1.
  42. Kwon, O.; Kim, G.H.; Ham, M.J.; Kim, W.; Kim, G.-H.; Cho, J.-H.; Kim, N.I.; Kim, K. Kangil A deep neural network for classification of melt-pool images in metal additive manufacturing. J. Intell. Manuf. 2020, 31, 375–386.
  43. Baumers, M.; Dickens, P.; Tuck, C.; Hague, R. The cost of additive manufacturing: Machine productivity, economies of scale and technology-push. Technol. Forecast. Soc. Chang. 2016, 102, 193–201.
  44. Ye, D.; Hsi Fuh, J.Y.; Zhang, Y.; Hong, G.S.; Zhu, K. In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks. ISA Trans. 2018, 81, 96–104.
  45. Zhang, Y.; Hong, G.S.; Ye, D.; Zhu, K.; Fuh, J.Y.H. Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Mater. Des. 2018, 156, 458–469.
  46. Zhang, Z.; Liu, Z.; Wu, D. Prediction of melt pool temperature in directed energy deposition using machine learning. Addit. Manuf. 2021, 37, 101692.
  47. Gunther, D.; Pirehgalin, M.F.; Weis, I.; Vogel-Heuser, B. Condition monitoring for the Binder Jetting AM-process with machine learning approaches. In Proceedings of the 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 10–12 June 2020; pp. 417–420.
  48. Wu, M.; Phoha, V.V.; Moon, Y.B.; Belman, A.K. Detecting malicious defects in 3d printing process using machine learning and image classification, ASME 2016 International Mechanical Engineering Congress and Exposition. Am. Soc. Mech. Eng. Digit. Collect. 2016.
  49. Li, L.; McGuan, R.; Isaac, R.; Kavehpour, P.; Candler, R. Improving precision of material extrusion 3D printing by in-situ monitoring & predicting 3D geometric deviation using conditional adversarial networks. Addit. Manuf. 2021, 38, 101695.
  50. Shevchik, S.A.; Kenel, C.; Leinenbach, C.; Wasmer, K. Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Addit. Manuf. 2018, 21, 598–604.
  51. Ye, D.; Hong, G.S.; Zhang, Y.; Zhu, K.; Fuh, J.Y.H. Defect detection in selective laser melting technology by acoustic signals with deep belief networks. Int. J. Adv. Manuf. Technol. 2018, 96, 2791–2801.
  52. Wu, H.; Yu, Z.; Wang, Y. A New Approach for Online Monitoring of Additive Manufacturing Based on Acoustic Emission. ASME Int. 2016.
  53. Wu, H.; Wang, Y.; Yu, Z. In situ monitoring of FDM machine condition via acoustic emission. Int. J. Adv. Manuf. Technol. 2016, 84, 1483–1495.
  54. Abdullah Al Faruqye, M.; Chhetri, S.R.; Canedo, A.; Wan, J. Acoustic side-channel attacks on additive manufacturing systems. In Proceedings of the 7th International Conference on Cyber-Physical Systems (ICCPS ’16), Vienna, Austria, 11–14 April 2016; pp. 1–10.
  55. Hojjati, A.; Adhikari, A.; Struckmann, K.; Chou, E.J.; Nguyen, T.N.T.; Madan, K.; Winslett, M.S.; Gunter, C.A.; King, W.P. Leave your phone at the door: Side channels that reveal factory floor secrets. Proc. ACM Conf. Comput. Commun. Secur. 2016, 883–894.
  56. Tang, Y.; Dong, G.; Zhou, Q.; Zhao, Y.F. Lattice Structure Design and Optimization with Additive Manufacturing Constraints. IEEE Trans. Autom. Sci. Eng. 2018, 15, 1546–1562.
  57. Zhang, Y.; Dong, G.; Yang, S.; Zhao, Y.F. Machine learning assisted prediction of the manufacturability of laser-based powder bed fusion process. In Proceedings of the ASME Design Engineering Technical Conference; American Society of Mechanical Engineers (ASME), Anaheim, CA, USA, 18–21 August 2019; Volume 1.
  58. Munguía, J.; Ciurana, J.; Riba, C. Neural-network-based model for build-time estimation in selective laser sintering. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2009, 223, 995–1003.
  59. Omairi, A.; Ismail, Z.H. Towards machine learning for error compensation in additive manufacturing. Appl. Sci. 2021, 11, 2375.
  60. Chowdhury, S.; Mhapsekar, K.; Anand, S. Part Build Orientation Optimization and Neural Network-Based Geometry Compensation for Additive Manufacturing Process. J. Manuf. Sci. Eng. Trans. ASME 2018, 140.
  61. Khanzadeh, M.; Rao, P.; Jafari-Marandi, R.; Smith, B.K.; Tschopp, M.A.; Bian, L. Quantifying Geometric Accuracy with Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts. J. Manuf. Sci. Eng. Trans. ASME 2018, 140.
  62. Noriega, A.; Blanco, D.; Alvarez, B.J.; Garcia, A. Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. Int. J. Adv. Manuf. Technol. 2013, 69, 2301–2313.
  63. Charalampous, P.; Kostavelis, I.; Kontodina, T.; Tzovaras, D. Learning-based error modeling in FDM 3D printing process. Rapid Prototyp. J. 2021, 27, 507–517.
  64. Shen, Z.; Shang, X.; Zhao, M.; Dong, X.; Xiong, G.; Wang, F.Y. A learning-based framework for error compensation in 3D printing. IEEE Trans. Cybern. 2019, 49, 4042–4050.
  65. Choi, T.-Y. Machine Learning Based Predictive Modelling of Dimensional Quality in Depostion with SUS316L; Graduate School of UNIST: Ulsan, Korea, 2020.