[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

A review on machine learning in 3D printing: applications, potential, and challenges

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abdelrahman M, Reutzel EW, Nassar AR, Starr TL (2017) Flaw detection in powder bed fusion using optical imaging. Addit Manuf 15:1–11

    Google Scholar 

  • Aboulkhair NT, Everitt NM, Ashcroft I, Tuck C (2014) Reducing porosity in AlSi10Mg parts processed by selective laser melting. Addit Manuf 1:77–86

    Google Scholar 

  • Acharya R, Sharon JA, Staroselsky A (2017) Prediction of microstructure in laser powder bed fusion process. Acta Mater 124:360–371

    Google Scholar 

  • Al Jassmi H, Al Najjar F, Mourad A-HI (2018) Large-scale 3D printing: the way forward. In: IOP conference series: materials science and engineering. IOP Publishing

  • Asadi-Eydivand M, Solati-Hashjin M, Fathi A, Padashi M, Abu Osman NA (2016) Optimal design of a 3D-printed scaffold using intelligent evolutionary algorithms. Appl Soft Comput 39:36–47

    Google Scholar 

  • Bacha A, Sabry AH, Benhra J (2019) Fault diagnosis in the field of additive manufacturing (3D printing) using Bayesian networks. Int J Online Biomed Eng (iJOE) 15(3):110–123

    Google Scholar 

  • Bayraktar Ö, Uzun G, Çakiroğlu R, Guldas A (2017) Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks. Polym Adv Technol 28(8):1044–1051

    Google Scholar 

  • Benoit N, Rana H, Valamanesh A (2018) Applying machine learning for real time optimization of powder bed manufacturing. Worcester Polytechnic Institute, Worcester

    Google Scholar 

  • Bin Maidin S, Campbell I, Pei E (2012) Development of a design feature database to support design for additive manufacturing. Assem Autom 32(3):235–244

    Google Scholar 

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

    Google Scholar 

  • Caltanissetta F, Grasso M, Petrò S, Colosimo BM (2018) Characterization of in situ measurements based on layerwise imaging in laser powder bed fusion. Addit Manuf 24:183–199

    Google Scholar 

  • Chen H, Zhao YF (2015) Learning algorithm based modeling and process parameters recommendation system for binder jetting additive manufacturing process. In: ASME 2015 international design engineering technical conferences and computers and information in engineering conference

  • Chen Q, Guillemot G, Gandin C-A, Bellet M (2017) Three-dimensional finite element thermomechanical modeling of additive manufacturing by selective laser melting for ceramic materials. Addit Manuf 16:124–137

    Google Scholar 

  • Chowdhury S (2016) Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes. University of Cincinnati, Cincinnati

    Google Scholar 

  • Deng L, Feng B, Zhang Y (2018) An optimization method for multi-objective and multi-factor designing of a ceramic slurry: combining orthogonal experimental design with artificial neural networks. Ceram Int 44(13):15918–15923

    Google Scholar 

  • Ding D, Pan Z, Cuiuri D, Li H, van Duin S, Larkin N (2016a) Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing. Robot Comput Integr Manuf 39:32–42

    Google Scholar 

  • Ding D, Shen C, Pan Z, Cuiuri D, Li H, Larkin N, van Duin S (2016b) Towards an automated robotic arc-welding-based additive manufacturing system from CAD to finished part. Comput Aided Des 73:66–75

    Google Scholar 

  • Dong Y, Guo G (2014) Evaluation and selection approach for cloud manufacturing service based on template and global trust degree. Comput Integr Manuf Syst 20(1):207–214

    Google Scholar 

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

    Google Scholar 

  • Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT (2016) Review of in situ process monitoring and in situ metrology for metal additive manufacturing. Mater Des 95:431–445

    Google Scholar 

  • Faruque MAA (2016) Forensics of thermal side-channel in additive manufacturing systems. University of California, Irvine

    Google Scholar 

  • Fergani O, Berto F, Welo T, Liang S (2017) Analytical modelling of residual stress in additive manufacturing. Fatigue Fract Eng Mater Struct 40(6):971–978

    Google Scholar 

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

    Google Scholar 

  • Gan Z, Li H, Wolff SJ, Bennett JL, Hyatt G, Wagner GJ, Cao J, Liu WK (2019) Data-driven microstructure and microhardness design in additive manufacturing using a self-organizing map. Engineering 5(4):730–735

    Google Scholar 

  • Garg A, Lam JSL, Savalani MM (2016) A new variant of genetic programming in formulation of laser energy consumption model of 3D printing process. In: Muthu SS, Savalani MM (eds) Handbook of sustainability in additive manufacturing, vol 1. Springer, Singapore, pp 31–50

    Google Scholar 

  • Gassman EE, Powell SM, Kallemeyn NA, DeVries NA, Shivanna KH, Magnotta VA, Ramme AJ, Adams BD, Grosland NM (2008) Automated bony region identification using artificial neural networks: reliability and validation measurements. Skelet Radiol 37(4):313–319

    Google Scholar 

  • Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S (2018) Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit Manuf 21:517–528

    Google Scholar 

  • Gu GX, Chen C-T, Buehler MJ (2018a) De novo composite design based on machine learning algorithm. Extreme Mech Lett 18:19–28

    Google Scholar 

  • Gu GX, Chen C-T, Richmond DJ, Buehler MJ (2018b) Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horizons 5(5):939–945

    Google Scholar 

  • He H, Yang Y, Pan Y (2019) Machine learning for continuous liquid interface production: printing speed modelling. J Manuf Syst 50:236–246

    Google Scholar 

  • Huff TJ, Ludwig PE, Zuniga JM (2018) The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning. Expert Rev Med Devices 15(5):349–356

    Google Scholar 

  • Jafari-Marandi R, Khanzadeh M, Tian W, Smith B, Bian L (2019) From in situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing. J Manuf Syst 51:29–41

    Google Scholar 

  • Jiang Z, Liu Y, Chen H, Hu Q (2014) Optimization of process parameters for biological 3D printing forming based on BP neural network and genetic algorithm. In: 21st ISPE Inc. international conference on concurrent engineering

  • Kanko JA, Sibley AP, Fraser JM (2016) In situ morphology-based defect detection of selective laser melting through inline coherent imaging. J Mater Process Technol 231:488–500

    Google Scholar 

  • Khadilkar A, Wang J, Rai R (2019) Deep learning-based stress prediction for bottom-up SLA 3D printing process. Int J Adv Manuf Technol 102(5–8):2555–2569

    Google Scholar 

  • Khan Z, Kahin K, Rauf S, Ramirez-Calderon G, Papagiannis N, Abdulmajid M, Hauser C (2019) Optimization of a 3D bioprinting process using ultrashort peptide bioinks. Int J Bioprint 5(1):173

    Google Scholar 

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

    Google Scholar 

  • Khanzadeh M, Tian W, Yadollahi A, Doude HR, Tschopp MA, Bian L (2018b) Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams. Addit Manuf 23:443–456

    Google Scholar 

  • Koeppe A, Hernandez Padilla CA, Voshage M, Schleifenbaum JH, Markert B (2018) Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks. Manuf Lett 15:147–150

    Google Scholar 

  • Kuo CN, Chua CK, Peng PC, Chen YW, Sing SL, Huang S, Su YL (2020) Microstructure evolution and mechanical property response via 3D printing parameter development of Al–Sc alloy. Virtual Phys Prototyp 15(1):120–129

    Google Scholar 

  • Lao W, Li M, Wong TN, Tan MJ, Tjahjowidodo T (2020) Improving surface finish quality in extrusion-based 3D concrete printing using machine learning-based extrudate geometry control. Virtual Phys Prototyp 15(2):178–193

    Google Scholar 

  • Lee SH, Park WS, Cho HS, Zhang W, Leu MC (2001) A neural network approach to the modelling and analysis of stereolithography processes. Proc Inst Mech Eng Part B J Eng Manuf 215(12):1719–1733

    Google Scholar 

  • Le-Quang T, Shevchik S, Meylan B, Vakili-Farahani F, Olbinado M, Rack A, Wasmer K (2018) Why is in situ quality control of laser keyhole welding a real challenge? Procedia CIRP 74:649–653

    Google Scholar 

  • Li X-f, Dong J-h, Zhang Y-z (2009) Modeling and applying of RBF neural network based on fuzzy clustering and pseudo-inverse method. In: 2009 international conference on information engineering and computer science, Wuhan, China. IEEE

  • Li Z, Zhang Z, Shi J, Wu D (2019) Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robot Comput Integr Manuf 57:488–495

    Google Scholar 

  • Lim CW, Tan K, Zhu X (2018) The framework of combining artificial intelligence and construction 3D printing in civil engineering. In: MATEC web of conferences, vol 206

  • Liu T, Guessasma S, Zhu J, Zhang W, Nouri H, Belhabib S (2018) Microstructural defects induced by stereolithography and related compressive behaviour of polymers. J Mater Process Technol 251:37–46

    Google Scholar 

  • Ludwig M, Meyer G, Tastl I, Moroney N, Gottwals M (2018) An appearance uniformity metric for 3D printing. In: Proceedings of the 15th ACM symposium on applied perception. In: Vancouver, British Columbia, Canada, ACM, pp 1–8

  • Madara SR, Selvan CP (2017) Review of recent developments in 3-D printing of turbine blades. Eur J Adv Eng Technol 4(7):497–509

    Google Scholar 

  • Mai J, Zhang L, Tao F, Ren L (2016) Customized production based on distributed 3D printing services in cloud manufacturing. Int J Adv Manuf Technol 84(1–4):71–83

    Google Scholar 

  • Meng L, Zhao J, Lan X, Yang H, Wang Z (2020) Multi-objective optimisation of bio-inspired lightweight sandwich structures based on selective laser melting. Virtual Phys Prototyp 15(1):106–119

    Google Scholar 

  • Menon A, Póczos B, Feinberg AW, Washburn NR (2019) Optimization of silicone 3D printing with hierarchical machine learning. 3D Print Addit Manuf 6(4):181–189

    Google Scholar 

  • Mishbak HH, Cooper G, Bartolo PJDS (2019) Development and characterisation of a photocurable alginate bioink for 3D bioprinting. Int J Bioprint 5(2):189

    Google Scholar 

  • Mohamed OA, Masood SH, Bhowmik JL (2016) Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing. Adv Prod Eng Manag 11(3):227–238

    Google Scholar 

  • Munguía J, Ciurana J, Riba C (2009) Neural-network-based model for build-time estimation in selective laser sintering. ProcInst Mech Eng Part B J Eng Manuf 223(8):995–1003

    Google Scholar 

  • Nagarajan HPN, Mokhtarian H, Jafarian H, Dimassi S, Bakrani-Balani S, Hamedi A, Coatanéa E, Gary Wang G, Haapala KR (2019) Knowledge-based design of artificial neural network topology for additive manufacturing process modeling: a new approach and case study for fused deposition modeling. J Mech Des. https://doi.org/10.1115/1.4042084

    Article  Google Scholar 

  • Noriega A, Blanco D, Alvarez BJ, Garcia A (2013) Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. Int J Adv Manuf Technol 69(9–12):2301–2313

    Google Scholar 

  • Okaro IA, Jayasinghe S, Sutcliffe C, Black K, Paoletti P, Green PL (2019) Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit Manuf 27:42–53

    Google Scholar 

  • Petrov A, Pernot J, Giannini F, Falcidieno B, Véron P (2016) Mapping aesthetic properties to 3D free form shapes through the use of a machine learning based framework. IMATI Report Series, p 16

  • Pham G, Lee S-H, Kwon O-H, Kwon K-R (2018) Anti-3D weapon model detection for safe 3D printing based on convolutional neural networks and D2 shape distribution. Symmetry 10(4):90

    Google Scholar 

  • Qi X, Chen G, Li Y, Cheng X, Li C (2019) Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering 5(4):721–729

    Google Scholar 

  • Radzi S, Tan JHK, Tan GJS, Yeong WY, Ferenczi MA, Low-Beer N, Mogali SR (2020) Development of a three-dimensional printed heart from computed tomography images of a plastinated specimen for learning anatomy. Anat Cell Biol 53(1):48

    Google Scholar 

  • Rong-Ji W, Xin-hua L, Qing-ding W, Lingling W (2008) Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm. Int J Adv Manuf Technol 42(11–12):1035–1042

    Google Scholar 

  • Sanjayan JG, Nematollahi B (2019) Chapter 1—3D concrete printing for construction applications. In: Sanjayan JG, Nazari A, Nematollahi B (eds) 3D concrete printing technology. Butterworth-Heinemann, Oxford, pp 1–11

    Google Scholar 

  • Saqib S, Urbanic RJ, Aggarwal K (2014) Analysis of laser cladding bead morphology for developing additive manufacturing travel paths. Procedia CIRP 17:824–829

    Google Scholar 

  • Sarlo R, Tarazaga PA (2016) A neural network approach to 3D printed surrogate systems. Springer, Cham

    Google Scholar 

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

    Google Scholar 

  • Scime L, Beuth J (2018b) 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

    Google Scholar 

  • Scime L, Beuth J (2019) Using machine learning to identify in situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit Manuf 25:151–165

    Google Scholar 

  • Shen X, Yao J, Wang Y, Yang J (2004) Density prediction of selective laser sintering parts based on artificial neural network. In: International symposium on neural networks. Springer, Berlin

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

    Google Scholar 

  • Shi Y, Zhang Y, Baek S, De Backer W, Harik R (2018) Manufacturability analysis for additive manufacturing using a novel feature recognition technique. Comput-Aided Des Appl 15(6):941–952

    Google Scholar 

  • Sing SL, Wiria FE, Yeong WY (2018) Selective laser melting of titanium alloy with 50 wt% tantalum: effect of laser process parameters on part quality. Int J Refract Metal Hard Mater 77:120–127

    Google Scholar 

  • Snell R, Tammas-Williams S, Chechik L, Lyle A, Hernández-Nava E, Boig C, Panoutsos G, Todd I (2019) Methods for rapid pore classification in metal additive manufacturing. JOM 72:101–109

    Google Scholar 

  • Sood AK, Ohdar RK, Mahapatra SS (2009) Parametric appraisal of fused deposition modelling process using the grey Taguchi method. Proc Inst Mech Eng Part B J Eng Manuf 224(1):135–145

    Google Scholar 

  • Sood AK, Equbal A, Toppo V, Ohdar RK, Mahapatra SS (2012a) An investigation on sliding wear of FDM built parts. CIRP J Manuf Sci Technol 5(1):48–54

    Google Scholar 

  • Sood AK, Ohdar RK, Mahapatra SS (2012b) Experimental investigation and empirical modelling of FDM process for compressive strength improvement. J Adv Res 3(1):81–90

    Google Scholar 

  • Tan JHK, Sing SL, Yeong WY (2020) Microstructure modelling for metallic additive manufacturing: a review. Virtual Phys Prototyp 15(1):87–105

    Google Scholar 

  • Tapia G, Elwany A (2014) A review on process monitoring and control in metal-based additive manufacturing. J Manuf Sci Eng 136(6):060801-1

    Google Scholar 

  • Thompson A, Maskery I, Leach RK (2016) X-ray computed tomography for additive manufacturing: a review. Meas Sci Technol 27(7):072001

    Google Scholar 

  • Vahabli E, Rahmati S (2016) Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. Int J Precis Eng Manuf 17(12):1589–1603

    Google Scholar 

  • van Eijnatten M, van Dijk R, Dobbe J, Streekstra G, Koivisto J, Wolff J (2018) CT image segmentation methods for bone used in medical additive manufacturing. Med Eng Phys 51:6–16

    Google Scholar 

  • Vijayaraghavan V, Garg A, Lam JSL, Panda B, Mahapatra SS (2014) Process characterisation of 3D-printed FDM components using improved evolutionary computational approach. Int J Adv Manuf Technol 78(5–8):781–793

    Google Scholar 

  • Vosniakos G-C, Maroulis T, Pantelis D (2007) A method for optimizing process parameters in layer-based rapid prototyping. Proc Inst Mech Eng Part B J Eng Manuf 221(8):1329–1340

    Google Scholar 

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

    Google Scholar 

  • Wang C, Jiang N, Chen Z, Chen Y, Dong Q (2015a) Prediction of sintering strength for selective laser sintering of polystyrene using artificial neural network. J Donghua Univ 5:22

    Google Scholar 

  • Wang W, Wang Y, Williams W, Browne A (2015b) Secure cloud manufacturing: research challenges and a case study. In: IFIP workshop on emerging ideas and trends in engineering of cyber-physical systems (EITEC), in CPS Week, Seattle

  • Wasmer K, Le-Quang T, Meylan B, Shevchik SA (2018a) In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach. J Mater Eng Perform 28(2):666–672

    Google Scholar 

  • Wasmer K, Le-Quang T, Meylan B, Vakili-Farahani F, Olbinado M, Rack A, Shevchik S (2018b) Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach. Procedia CIRP 74:654–658

    Google Scholar 

  • Williams G, Meisel NA, Simpson TW, McComb C (2019) Design repository effectiveness for 3D convolutional neural networks: application to additive manufacturing. J Mech Des 141(11):e4044199

    Google Scholar 

  • Wohlkinger W, Vincze M (2011) Shape-based depth image to 3D model matching and classification with inter-view similarity. In: 2011 IEEE/RSJ international conference on intelligent robots and systems. IEEE

  • Wong KV, Hernandez A (2012) A review of additive manufacturing. International scholarly research notices

  • Wu M, Phoha VV, Moon YB, Belman AK (2016a) Detecting malicious defects in 3D printing process using machine learning and image classification. In: ASME 2016 international mechanical engineering congress and exposition

  • Wu Y, Peng G, Chen L, Zhang H (2016b) Service architecture and evaluation model of distributed 3D printing based on cloud manufacturing. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE

  • Wu M, Song Z, Moon YB (2017) Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. J Intell Manuf 30(3):1111–1123

    Google Scholar 

  • Wu H, Yu Z, Wang Y (2019) Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission. Measurement 136:445–453

    Google Scholar 

  • Xiong J, Zhang G, Hu J, Wu L (2014) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf 25(1):157–163

    Google Scholar 

  • Yamanaka Y, Todoroki A, Ueda M, Hirano Y, Matsuzaki R (2016) Fiber line optimization in single ply for 3D printed composites. Open J Compos Mater 06(04):121–131

    Google Scholar 

  • Yang J, Gu D, Lin K, Ma C, Wang R, Zhang H, Guo M (2020) Laser 3D printed bio-inspired impact resistant structure: failure mechanism under compressive loading. Virtual Phys Prototyp 15(1):75–86

    Google Scholar 

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

    Google Scholar 

  • Ye D, Hsi Fuh JY, Zhang Y, Hong GS, Zhu K (2018) In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks. ISA Trans 81:96–104

    Google Scholar 

  • Yu C, Jiang J (2020) A perspective on using machine learning in 3D bioprinting. Int J Bioprint 6(1):95

    Google Scholar 

  • Yu WH, Sing SL, Chua CK, Kuo CN, Tian XL (2019a) Influence of re-melting on surface roughness and porosity of AlSi10Mg parts fabricated by selective laser melting. J Alloys Compd 792:574–581

    Google Scholar 

  • Yu WH, Sing SL, Chua CK, Kuo CN, Tian XL (2019b) Particle-reinforced metal matrix nanocomposites fabricated by selective laser melting: a state of the art review. Prog Mater Sci 104:330–379

    Google Scholar 

  • Yuan B, Guss GM, Wilson AC, Hau-Riege SP, DePond PJ, McMains S, Matthews MJ, Giera B (2018) Machine-learning-based monitoring of laser powder bed fusion. Adv Mater Technol 3(12):1800136

    Google Scholar 

  • Zhang X, Le X, Panotopoulou A, Whiting E, Wang CCL (2015) Perceptual models of preference in 3D printing direction. ACM Trans Graph 34(6):1–12

    Google Scholar 

  • Zhang W, Mehta A, Desai PS, Higgs C (2017) Machine learning enabled powder spreading process map for metal additive manufacturing (AM). In: International Solid Free Form Fabrication Symposium Austin, TX

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

    Google Scholar 

  • Zhao C, Fezzaa K, Cunningham RW, Wen H, De Carlo F, Chen L, Rollett AD, Sun T (2017) Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction. Sci Rep 7(1):3602

    Google Scholar 

  • Zohdi TI (2018) Dynamic thermomechanical modeling and simulation of the design of rapid free-form 3D printing processes with evolutionary machine learning. Comput Methods Appl Mech Eng 331:343–362

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme.

Author information

Authors and Affiliations

  1. Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    G. D. Goh, S. L. Sing & W. Y. Yeong

Authors
  1. G. D. Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. L. Sing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. W. Y. Yeong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to W. Y. Yeong.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goh, G.D., Sing, S.L. & Yeong, W.Y. A review on machine learning in 3D printing: applications, potential, and challenges. Artif Intell Rev 54, 63–94 (2021). https://doi.org/10.1007/s10462-020-09876-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-020-09876-9

Keywords