[ 1 ] Lu B, Li D, Tian X. Development trends in additive manufacturing and 3D printing. Engineering 2015;1(1):85–9. link1

[ 2 ] Derby B. Additive manufacture of ceramics components by inkjet printing. Engineering 2015;1(1):113–23. link1

[ 3 ] Gu D, Ma C, Xia M, Dai D, Shi Q. A multiscale understanding of the thermodynamic and kinetic mechanisms of laser additive manufacturing. Engineering 2017;3(5):675–84. link1

[ 4 ] Herzog D, Seyda V, Wycisk E, Emmelmann C. Additive manufacturing of metals. Acta Mater 2016;117(15):371–92. link1

[ 5 ] Liu L, Ding Q, Zhong Y, Zou J, Wu J, Chiu YL, et al. Dislocation network in additive manufactured steel breaks strength–ductility trade-off. Mater Today 2018;21(4):354–61. link1

[ 6 ] Gorsse S, Hutchinson C, Gouné M, Banerjee R. Additive manufacturing of metals: a brief review of the characteristic microstructures and properties of steels, Ti–6Al–4V and high-entropy alloys. Sci Technol Adv Mater 2017;18 (1):584–610. link1

[ 7 ] Acharya R, Sharon JA, Staroselsky A. Prediction of microstructure in laser powder bed fusion process. Acta Mater 2017;124:360–71. link1

[ 8 ] Fergani O, Berto F, Welo T, Liang SY. Analytical modelling of residual stress in additive manufacturing. Fatigue Fract Eng Mater Struct 2017;40(6):971–8. link1

[ 9 ] Chen Q, Guillemot G, Gandin CA, Bellet M. Three-dimensional finite element thermomechanical modeling of additive manufacturing by selective laser melting for ceramic materials. Addit Manuf 2017;16:124–37. link1

[10] Kohavi R, Provost F. Glossary of terms. Mach Learn 1998;30(2–3):271–4. link1

[11] Géron A. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. Boston: O’Reilly Media, Inc.; 2017. link1

[12] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44. link1

[13] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in Neural Information Processing Systems 25: Proceedings of Neural Information Processing Systems 2012; 2012 Dec 3–6; Lake Tahoe, NV, USA. p. 1097–105. link1

[14] Anusuya MA, Katti SK. Speech recognition by machine, a review. 2010. arXiv:1001.2267.

[15] Devlin J, Chang M, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. 2018. arXiv:1810.04805.

[16] Ondruska P, Posner I. Deep tracking: seeing beyond seeing using recurrent neural networks. 2016. arXiv: 1602.00991.

[17] ISO/ASTM52900-15: Standard terminology for additive manufacturing— general principles—terminology. ASTM standard. West Conshohocken: ASTM International; 2015.

[18] King WE, Anderson AT, Ferencz RM, Hodge NE, Kamath C, Khairallah SA, et al. Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. Appl Phys Rev 2015;2(4):041304. link1

[19] Gibson I, Rosen D, Stucker B. Binder jetting. In: Gibson I, Rosen D, Stucker B, editors. Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing. New York: Springer; 2015. p. 205–18. link1

[20] Chacón JM, Caminero M, García-Plaza E, Núñez P. Additive manufacturing of PLA structures using fused deposition modelling: effect of process parameters on mechanical properties and their optimal selection. Mater Des 2017;124:143–57. link1

[21] Kruth JP, Wang X, Laoui T, Froyen L. Lasers and materials in selective laser sintering. Assem Autom 2003;23(4):357–71. link1

[22] Kruth JP, Mercelis P, Van Vaerenbergh J, Froyen L, Rombouts M. Binding mechanisms in selective laser sintering and selective laser melting. Rapid Prototyping J 2005;11(1):26–36. link1

[23] Murr LE, Gaytan SM, Ramirez DA, Martinez E, Hernandez J, Amato KN, et al. Metal fabrication by additive manufacturing using laser and electron beam melting technologies. J Mater Sci Technol 2012;28(1):1–14. link1

[24] Bai Y, Williams CB. An exploration of binder jetting of copper. Rapid Prototyping J 2015;21(2):177–85. link1

[25] Sood AK, Ohdar RK, Mahapatra S. Parametric appraisal of mechanical property of fused deposition modelling processed parts. Mater Des 2010;31(1):287–95. link1

[26] Goldberg Y. Neural network methods for natural language processing. Synth Lect Hum Lang Technol 2017;10(1):1–309. link1

[27] Rumerlhart DE, Hinton GE, Williams RJ. Learning representations by backpropagating errors. Nature 1986;323(6088):533–6. link1

[28] Gardner MW, Dorling S. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 1998;32(14–5):2627–36. link1

[29] Mikolov T, Karafiát M, Burget L, Cˇernocky´ J, Khudanpur S. Recurrent neural network based language model. In: Proceedings of the 11th Annual Conference of the International Speech Communication Association; 2010 Sep 26–30; Makuhari, Japan; 2010.

[30] Chowdhury S, Anand S. Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes. In: Proceedings of the 11th International Manufacturing Science and Engineering Conference; 2016 June 27– July 1; Blacksburg, VA, USA; 2016.

[31] Koeppe A, Hernandez Padilla CA, Voshage M, Schleifenbaum JH, Markert B. Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks. Manuf Lett 2018;15:147–50. link1

[32] McComb C, Meisel N, Murphy C, Simpson TW. Predicting part mass, required support material, and build time via autoencoded voxel patterns. EngrXiv. Epub 2018 Jul 4.

[33] Li H, Ma X, Rathore AS, Li Z, An Q, Song C, et al. Image dataset for visual objects classification in 3D printing. 2018. arXiv:1803.00391.

[34] Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Mater Des 2016;95:431–45. link1

[35] Shevchik SA, 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. link1

[36] Wasmer K, Le-Quang T, Meylan B, Shevchik SA. In situ quality monitoring in AM using acoustic emission: a machine learning approach. J Mater Eng Perform 2019;28(2):666–72. link1

[37] Zhang Y, Hong GS, Ye D, Zhu K, Fuh JY. Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Mater Des 2018;156:458–69. link1

[38] Wang T, Kwok TH, Zhou C, Vader S. In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. J Manuf Syst 2018;47:83–92. link1

[39] Sood AK, Ohdar RK, Mahapatra SS. Experimental investigation and empirical modelling of FDM process for compressive strength improvement. J Adv Res 2012;3(1):81–90. link1

[40] Sood AK, Equbal A, Toppo V, Ohdar R, Mahapatra S. An investigation on sliding wear of FDM built parts. CIRP J Manuf Sci Technol 2012;5(1):48–54. link1

[41] Vosniakos GC, Maroulis T, Pantelis D. A method for optimizing process parameters in layer-based rapid prototyping. Proc Inst Mech Eng B J Eng Manuf 2007;221(8):1329–40. link1

[42] Equbal A, Sood AK, Mahapatra S. Prediction of dimensional accuracy in fused deposition modelling: a fuzzy logic approach. Int J Product Qual Manag 2011;7 (1):22–43. link1

[43] Sood AK, Ohdar RK, Mahapatra SS. Parametric appraisal of fused deposition modelling process using the grey taguchi method. Proc Inst Mech Eng B J Eng Manuf 2010;224(1):135–45. link1

[44] Chen H, Zhao YF. Learning algorithm based modeling and process parameters recommendation system for binder jetting additive manufacturing process. In: Proceedings of 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; 2015 Aug 2–5; Boston, MA, USA; 2015. p. V01AT02A029.

[45] Shen X, Yao J, Wang Y, Yang J. Density prediction of selective laser sintering parts based on artificial neural network. In: Yin FL, Wang J, Guo C, editors. Advances in neural networks—ISNN 2004. Berlin: Springer; 2004. p. 832–40.

[46] Li XF, Dong JH, Zhang YZ. Modeling and applying of RBF neural network based on fuzzy clustering and pseudo-inverse method. In: Proceedings of 2009 International Conference on Information Engineering and Computer Science; 2009 Dec 19–20; Wuhan, China; 2009.

[47] Munguía J, Ciurana J, Riba C. Neural-network-based model for build-time estimation in selective laser sintering. Proc Inst Mech Eng B J Eng Manuf 2009;223(8):995–1003. link1

[48] Wang RJ, Li XH, Wu QD, Wang LL. Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm. Int J Adv Manuf Technol 2009;42(11–12):1035–42. link1

[49] Garg A, Tai K, Savalani MM. State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyp J 2014;20(2):164–78. link1

[50] Wang CY, Jiang N, Chen ZL, Chen Y, Dong Q. Prediction of sintering strength for selective laser sintering of polystyrene using artificial neural network. J Donghua Universit 2015;5:825–30. link1

[51] Wang RJ, Li J, Wang F, Li X, Wu Q. Ann model for the prediction of density in selective laser sintering. Int J Manuf Res 2009;4(3):362–73. link1

[52] Lee SH, Park WS, Cho HS, Zhang W, Leu MC. A neural network approach to the modelling and analysis of stereolithography processes. Proc Inst Mech Eng B J Eng Manuf 2001;215(12):1719–33. link1

[53] Caiazzo F, Caggiano A. Laser direct metal deposition of 2024 Al alloy: trace geometry prediction via machine learning. Materials 2018;11(3):444. link1

[54] Zhang W, Mehta A, Desai PS, Higgs III CF. Machine learning enabled powder spreading process map for metal additive manufacturing (AM). In: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium; 2017 Aug 7–9; Austin, TX, USA. 2017. p. 1235–49. link1

[55] 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;252:838–48. link1

[56] Xu S, Chen L. A novel approach for determining the optimal number of hidden layer neurons for FNN’s and its application in data mining. In: Proceedings of the 5th International Conference on Information Technology and Applications; 2008 June 23–26; Cairns, Australia. 2008. p. 683–6. link1

[57] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. 2015. arXiv: 1502.03167.

[58] Deng J, Dong W, Socher R, Li LJ, Li K, Li FF. ImageNet: a large-scale hierarchical image database. In: Proceedings of 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2009 June 20–25; Miami, FL, USA. 2009. p. 248–55. link1

[59] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86(11):2278–324. link1

[60] Rajpurkar P, Zhang J, Lopyrev K, Liang P. SQuAD: 100,000+ questions for machine comprehension of text. 2016. arXiv:1606.05250.

[61] Abu-El-Haija S, Kothari N, Lee J, Natsev P, Toderici G, Varadarajan B, et al. YouTube-8M: a large-scale video classification benchmark. 2016. arXiv:1609.08675.

[62] Kingma DP, Welling M. Auto-encoding variational bayes. 2013. arXiv:1312.6114.

[63] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems 27; 2014 Dec 8–13; Montreal, QC, Canada. 2014. p. 2672–80. link1

[64] Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial autoencoders. 2015. arXiv:1511.05644.

[65] Yusuf SM, Gao N. Influence of energy density on metallurgy and properties in metal additive manufacturing. Mater Sci Technol 2017;33(11):1269–89. link1

[66] Ng AY. Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings of the 21st International Conference on Machine Learning; 2004 July 4–8; Banff, AB, Canada; 2004.

[67] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15(1):1929–58. link1

[68] Ramakrishna S, Zhang TY, Lu WC, Qian Q, Low JSC, Yune JHR, et al. Materials informatics. J Intell Manuf. 2019;30(6):2307–26.

[69] Lu Y, Witherell P, Donmez A. A collaborative data management system for additive manufacturing. In: Proceedings of ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; 2017 Aug 6–9; Cleveland, Oh, USA; 2017.

[70] Cheng Y, Wang D, Zhou P, Zhang T. A survey of model compression and acceleration for deep neural networks. 2017. arXiv:1710.09282.

[71] Azimi SM, Britz D, Engstler M, Fritz M, Mücklich F. Advanced steel microstructural classification by deep learning methods. Sci Rep 2018;8 (1):2128. link1

[72] Popova E, Rodgers TM, Gong X, Cecen A, Madison JD, Kalidindi SR. Processstructure linkages using a data science approach: application to simulated additive manufacturing data. Integr Mater Manuf Innov 2017;6(1):54–68. link1

[73] Rodgers T. Exploration of process-structure linkages in simulated additive manufacturing microstructures. Harvard Dataverse 2015. link1

[74] Karpatne A, Atluri G, Faghmous JH, Steinbach M, Banerjee A, Ganguly A, et al. Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans Knowl Data Eng 2017;29(10):2318–31. link1