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《工程(英文)》 >> 2019年 第5卷 第4期 doi: 10.1016/j.eng.2019.04.012

将基于神经网络的机器学习方法应用于增材制造——应用现状、当前挑战和未来前景

a State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
b Corporate Technology, Siemens Ltd., China, Beijing 100102, China

收稿日期: 2018-07-28 修回日期: 2018-10-22 录用日期: 2019-04-08 发布日期: 2019-07-03

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摘要

增材制造,也称为3D打印,由于其与传统减材制造相比具有独特的优势,因而越来越受到学术界和工业界的关注。然而,增材制造工艺参数难以调整,因为这些参数会对打印的微观结构和后续产品的性能产生巨大影响。使用传统的数值分析模型为增材制造建立流程-结构-属性-性能的对应关系也是一项艰巨的任务。而今,机器学习方法已成为执行复杂模式识别和回归分析的一种有效方式,并且它无需构建和处理潜在的物理模型。得益于当前庞大的数据集、计算能力的提高和计算模型的优化改善,神经网络算法成为了机器学习算法中使用最广泛的模型。本文综述了神经网络算法在增材制造全链条中的模型设计、实时监测、质量评价等方面的应用进展。然后,本文概述了当前将神经网络应用于增材制造所遇到的挑战以及针对这些问题的可能解决方案。最后,提出了未来趋势,以对这一跨学科领域进行全面讨论。

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参考文献

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

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

[ 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. 链接1

[ 4 ] Herzog D, Seyda V, Wycisk E, Emmelmann C. Additive manufacturing of metals. Acta Mater 2016;117(15):371–92. 链接1

[ 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. 链接1

[ 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. 链接1

[ 7 ] Acharya R, Sharon JA, Staroselsky A. Prediction of microstructure in laser powder bed fusion process. Acta Mater 2017;124:360–71. 链接1

[ 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. 链接1

[ 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. 链接1

[10] Kohavi R, Provost F. Glossary of terms. Mach Learn 1998;30(2–3):271–4. 链接1

[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. 链接1

[12] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[21] Kruth JP, Wang X, Laoui T, Froyen L. Lasers and materials in selective laser sintering. Assem Autom 2003;23(4):357–71. 链接1

[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. 链接1

[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. 链接1

[24] Bai Y, Williams CB. An exploration of binder jetting of copper. Rapid Prototyping J 2015;21(2):177–85. 链接1

[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. 链接1

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

[27] Rumerlhart DE, Hinton GE, Williams RJ. Learning representations by backpropagating errors. Nature 1986;323(6088):533–6. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[53] Caiazzo F, Caggiano A. Laser direct metal deposition of 2024 Al alloy: trace geometry prediction via machine learning. Materials 2018;11(3):444. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[59] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86(11):2278–324. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[73] Rodgers T. Exploration of process-structure linkages in simulated additive manufacturing microstructures. Harvard Dataverse 2015. 链接1

[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. 链接1

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