[1] Moradi M, Mehrabi O, Azdast T et al. Enhancement of low power CO2 laser cutting process for injection molded polycarbonate[J]. Optics & Laser Technology, 96, 208-218(2017).

[2] Schulz W, Becker D, Franke J et al. Heat conduction losses in laser cutting of metals[J]. Journal of Physics D: Applied Physics, 26, 1357-1363(1993).

[3] Choudhury I A, Shirley S. Laser cutting of polymeric materials: an experimental investigation[J]. Optics & Laser Technology, 42, 503-508(2010).

[4] Rauh B, Kreling S, Kolb M et al. UV-laser cleaning and surface characterization of an aerospace carbon fibre reinforced polymer[J]. International Journal of Adhesion and Adhesives, 82, 50-59(2018).

[5] Shi T Y, Wang C M, Mi G Y et al. A study of microstructure and mechanical properties of aluminum alloy using laser cleaning[J]. Journal of Manufacturing Processes, 42, 60-66(2019).

[6] Park H K, Grigoropoulos C P, Leung W P et al. A practical excimer laser-based cleaning tool for removal of surface contaminants[J]. IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A, 17, 631-643(1994).

[7] Salonitis K, Stournaras A, Tsoukantas G et al. A theoretical and experimental investigation on limitations of pulsed laser drilling[J]. Journal of Materials Processing Technology, 183, 96-103(2007).

[8] Gautam G D, Pandey A K. Pulsed Nd∶YAG laser beam drilling: a review[J]. Optics & Laser Technology, 100, 183-215(2018).

[9] Hwang D J, Choi T Y, Grigoropoulos C P. Liquid-assisted femtosecond laser drilling of straight and three-dimensional microchannels in glass[J]. Applied Physics A, 79, 605-612(2004).

[10] Yue Y F, Kurokawa T. Designing responsive photonic crystal patterns by using laser engraving[J]. ACS Applied Materials & Interfaces, 11, 10841-10847(2019).

[11] Uh J, Lee J S, Kim Y H et al. Laser engraving of micro-patterns on roll surfaces[J]. ISIJ International, 42, 1266-1272(2002).

[12] Lahoz R, de la Fuente G F, Pedra J M et al. Laser engraving of ceramic tiles[J]. International Journal of Applied Ceramic Technology, 8, 1208-1217(2011).

[13] Ravi-Kumar S, Lies B, Zhang X A et al. Laser ablation of polymers: a review[J]. Polymer International, 68, 1391-1401(2019).

[14] Preuss S, Demchuk A, Stuke M. Sub-picosecond UV laser ablation of metals[J]. Applied Physics A, 61, 33-37(1995).

[15] Vogel A, Venugopalan V. Mechanisms of pulsed laser ablation of biological tissues[J]. Chemical Reviews, 103, 577-644(2003).

[16] Scotti G, Matilainen V, Kanninen P et al. Laser additive manufacturing of stainless steel micro fuel cells[J]. Journal of Power Sources, 272, 356-361(2014).

[17] Zhu Y Y, Li J, Tian X J et al. Microstructure and mechanical properties of hybrid fabricated Ti-6.5Al-3.5Mo-1.5Zr-0.3Si titanium alloy by laser additive manufacturing[J]. Materials Science and Engineering: A, 607, 427-434(2014).

[18] Leung C L A, Marussi S, Towrie M et al. The effect of powder oxidation on defect formation in laser additive manufacturing[J]. Acta Materialia, 166, 294-305(2019).

[19] Akman E, Demir A, Canel T et al. Laser welding of Ti6Al4V titanium alloys[J]. Journal of Materials Processing Technology, 209, 3705-3713(2009).

[20] Cao X J, Jahazi M, Immarigeon J P et al. A review of laser welding techniques for magnesium alloys[J]. Journal of Materials Processing Technology, 171, 188-204(2006).

[21] Hong K M, Shin Y C. Prospects of laser welding technology in the automotive industry: a review[J]. Journal of Materials Processing Technology, 245, 46-69(2017).

[22] Samant A N, Dahotre N B. Laser machining of structural ceramics: a review[J]. Journal of the European Ceramic Society, 29, 969-993(2009).

[23] Yang L L, Wei J T, Ma Z et al. The fabrication of micro/nano structures by laser machining[J]. Nanomaterials, 9, 1789(2019).

[24] Meijer J, Du K, Gillner A et al. Laser machining by short and ultrashort pulses, state of the art and new opportunities in the age of the photons[J]. CIRP Annals, 51, 531-550(2002).

[25] Gattass R R, Mazur E. Femtosecond laser micromachining in transparent materials[J]. Nature Photonics, 2, 219-225(2008).

[26] Negarestani R, Sundar M, Sheikh M A et al. Numerical simulation of laser machining of carbon-fibre-reinforced composites[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224, 1017-1027(2010).

[27] Zhong Y G, Xue K, Shi D Y. An improved artificial neural network for laser welding parameter selection and prediction[J]. The International Journal of Advanced Manufacturing Technology, 68, 755-762(2013).

[28] Velli M C, Tsibidis G D, Mimidis A et al. Predictive modeling approaches in laser-based material processing[J]. Journal of Applied Physics, 128, 183102(2020).

[29] Vagheesan S, Govindarajalu J. Hybrid neural network-particle swarm optimization algorithm and neural network-genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41, 1-15(2019).

[30] Teixidor D, Grzenda M, Bustillo A et al. Modeling pulsed laser micromachining of micro geometries using machine-learning techniques[J]. Journal of Intelligent Manufacturing, 26, 801-814(2015).

[31] Wang R J, Li X H, Wu Q D et al. Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm[J]. The International Journal of Advanced Manufacturing Technology, 42, 1035-1042(2009).

[32] Rajaram N, Sheikh-Ahmad J, Cheraghi S H. CO2 laser cut quality of 4130 steel[J]. International Journal of Machine Tools and Manufacture, 43, 351-358(2003).

[33] Park Y W, Rhee S. Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation[J]. The International Journal of Advanced Manufacturing Technology, 37, 1014-1021(2008).

[34] Omidvar M, Fard R K, Sohrabpoor H et al. Selection of laser bending process parameters for maximal deformation angle through neural network and teaching-learning-based optimization algorithm[J]. Soft Computing, 19, 609-620(2015).

[35] Olabi A G, Casalino G, Benyounis K Y et al. An ANN and Taguchi algorithms integrated approach to the optimization of CO2 laser welding[J]. Advances in Engineering Software, 37, 643-648(2006).

[36] Nakhjavani O B, Ghoreishi M. Multi criteria optimization of laser percussion drilling process using artificial neural network model combined with genetic algorithm[J]. Materials and Manufacturing Processes, 21, 11-18(2006).

[37] McDonnell M D T, Arnaldo D, Pelletier E et al. Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining[J]. Journal of Intelligent Manufacturing, 32, 1471-1483(2021).

[38] Li K S, Ma Z Y, Fu P et al. Quantitative evaluation of surface crack depth with a scanning laser source based on particle swarm optimization-neural network[J]. NDT & E International, 98, 208-214(2018).

[39] Huang Y, Liu Y, Zhu G L. Parameter optimization of laser scribing technics of 30Q130 grain-oriented silicon steel based on genetic neural network[J]. Applied Mechanics and Materials, 37/38, 844-848(2010).

[40] Günther J, Pilarski P M, Helfrich G et al. First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning[J]. Procedia Technology, 15, 474-483(2014).

[41] Ghosal S, Chaki S. Estimation and optimization of depth of penetration in hybrid CO2 laser-MIG welding using ANN-optimization hybrid model[J]. The International Journal of Advanced Manufacturing Technology, 47, 1149-1157(2010).

[42] Ding H, Wang Z C, Guo Y C. Multi-objective optimization of fiber laser cutting based on generalized regression neural network and non-dominated sorting genetic algorithm[J]. Infrared Physics & Technology, 108, 103337(2020).

[43] Dhara S K, Kuar A S, Mitra S. An artificial neural network approach on parametric optimization of laser micro-machining of die-steel[J]. The International Journal of Advanced Manufacturing Technology, 39, 39-46(2008).

[44] Zhang Y Q, Chen W Z, Zhang X D et al. Synthetic evaluation and neural-network prediction of laser cutting quality[J]. Proceedings of SPIE, 5629, 237-246(2005).

[45] Ciurana J, Arias G, Ozel T. Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel[J]. Materials and Manufacturing Processes, 24, 358-368(2009).

[46] Biswas R, Kuar A S, Biswas S K et al. Artificial neural network modelling of Nd∶YAG laser microdrilling on titanium nitride‒alumina composite[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224, 473-482(2010).

[47] Zhang Y J, Hong G S, Ye D S et al. Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring[J]. Materials & Design, 156, 458-469(2018).

[48] You D Y, Gao X D, Katayama S. WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM[J]. IEEE Transactions on Industrial Electronics, 62, 628-636(2015).

[49] Ye D S, Hsi Fuh J Y, Zhang Y J et al. In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks[J]. ISA Transactions, 81, 96-104(2018).

[50] Scime L, Siddel D, Baird S et al. Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: a machine-agnostic algorithm for real-time pixel-wise semantic segmentation[J]. Additive Manufacturing, 36, 101453(2020).

[51] Scime L, Beuth J. Using machine learning to identify in situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process[J]. Additive Manufacturing, 25, 151-165(2019).

[52] Scime L, Beuth J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm[J]. Additive Manufacturing, 19, 114-126(2018).

[53] Scime L, Beuth J. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process[J]. Additive Manufacturing, 24, 273-286(2018).

[54] Okaro I A, Jayasinghe S, Sutcliffe C et al. Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning[J]. Additive Manufacturing, 27, 42-53(2019).

[55] Khanzadeh M, Chowdhury S, Tschopp M A et al. In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes[J]. IISE Transactions, 51, 437-455(2019).

[56] Jafari-Marandi R, Khanzadeh M, Tian W et al. From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing[J]. Journal of Manufacturing Systems, 51, 29-41(2019).

[57] Grasso M, Demir A G, Previtali B et al. In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume[J]. Robotics and Computer-Integrated Manufacturing, 49, 229-239(2018).

[58] Caggiano A, Zhang J J, Alfieri V et al. Machine learning-based image processing for on-line defect recognition in additive manufacturing[J]. CIRP Annals, 68, 451-454(2019).

[59] Ye D S, Hong G S, Zhang Y J et al. Defect detection in selective laser melting technology by acoustic signals with deep belief networks[J]. The International Journal of Advanced Manufacturing Technology, 96, 2791-2801(2018).

[60] Wu H X, Yu Z H, Wang Y. Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model[J]. The International Journal of Advanced Manufacturing Technology, 90, 2027-2036(2017).

[61] Shevchik S A, Kenel C, Leinenbach C et al. Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks[J]. Additive Manufacturing, 21, 598-604(2018).

[62] Xie Y H, Praeger M, Grant-Jacob J A et al. Motion control for laser machining via reinforcement learning[J]. Optics Express, 30, 20963-20979(2022).

[63] Jordan M I, Mitchell T M. Machine learning: trends, perspectives, and prospects[J]. Science, 349, 255-260(2015).

[64] Mitchell T M[M]. Machine learning(1997).

[65] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

[66] Zhou Z H[M]. Machine Learning(2021).

[67] Goodfellow I, Bengio Y, Courville A[M]. Deep learning(2016).

[68] Cybenko G. Approximation by superpositions of a sigmoidal function[J]. Mathematics of Control, Signals and Systems, 2, 303-314(1989).

[69] Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators[J]. Neural Networks, 2, 359-366(1989).

[70] Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks[J]. Neural Networks, 3, 551-560(1990).

[71] Mills B, Heath D J, Grant-Jacob J A et al. Predictive capabilities for laser machining via a neural network[J]. Optics Express, 26, 17245-17253(2018).

[72] McDonnell M D T, Grant-Jacob J A, Praeger M et al. Identification of spatial intensity profiles from femtosecond laser machined depth profiles via neural networks[J]. Optics Express, 29, 36469-36486(2021).

[73] Heath D J, Grant-Jacob J A, Xie Y H et al. Machine learning for 3D simulated visualization of laser machining[J]. Optics Express, 26, 21574-21584(2018).

[74] Zhang D K, Guo L, Karniadakis G E. Learning in modal space: solving time-dependent stochastic PDEs using physics-informed neural networks[J]. SIAM Journal on Scientific Computing, 42, A639-A665(2020).

[75] Sirignano J, Spiliopoulos K. DGM: a deep learning algorithm for solving partial differential equations[J]. Journal of Computational Physics, 375, 1339-1364(2018).

[76] Sheng H L, Yang C. PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries[J]. Journal of Computational Physics, 428, 110085(2021).

[77] Raissi M, Perdikaris P, Karniadakis G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 378, 686-707(2019).

[78] Raissi M, Karniadakis G E. Hidden physics models: machine learning of nonlinear partial differential equations[J]. Journal of Computational Physics, 357, 125-141(2018).

[79] Mao Z P, Jagtap A D, Karniadakis G E. Physics-informed neural networks for high-speed flows[J]. Computer Methods in Applied Mechanics and Engineering, 360, 112789(2020).

[80] Karniadakis G E, Kevrekidis I G, Lu L et al. Physics-informed machine learning[J]. Nature Reviews Physics, 3, 422-440(2021).

[81] Xie Y H, Heath D J, Grant-Jacob J A et al. Deep learning for the monitoring and process control of femtosecond laser machining[J]. Journal of Physics: Photonics, 1, 035002(2019).

[82] Kwon O, Kim H G, Ham M J et al. A deep neural network for classification of melt-pool images in metal additive manufacturing[J]. Journal of Intelligent Manufacturing, 31, 375-386(2020).

[83] Yang B, Ma D N, Liu W W et al. Deep-learning-based colorimetric polarization-angle detection with metasurfaces[J]. Optica, 9, 217-220(2022).

[84] Scime L, Beuth J. Melt pool geometry and morphology variability for the Inconel 718 alloy in a laser powder bed fusion additive manufacturing process[J]. Additive Manufacturing, 29, 100830(2019).

[85] Ren Z S, Gao L, Clark S J et al. Machine learning-aided real-time detection of keyhole pore generation in laser powder bed fusion[J]. Science, 379, 89-94(2023).

[86] Balling P, Schou J. Femtosecond-laser ablation dynamics of dielectrics: basics and applications for thin films[J]. Reports on Progress in Physics, 76, 036502(2013).

[87] Déziel J L, Dubé L J, Varin C. Dynamical rate equation model for femtosecond laser-induced breakdown in dielectrics[J]. Physical Review B, 104, 045201(2021).

[88] Gruzdev V E, Komolov V L, Przhibel’skiĭ S G. Ionization of nanoparticles by supershort moderate-intensity laser pulses[J]. Journal of Optical Technology, 81, 256-261(2014).

[89] Stoian R. Volume photoinscription of glasses: three-dimensional micro- and nanostructuring with ultrashort laser pulses[J]. Applied Physics A, 126, 1-30(2020).

[90] Zhang Z Y, Liu Z C, Wu D Z. Prediction of melt pool temperature in directed energy deposition using machine learning[J]. Additive Manufacturing, 37, 101692(2021).

[91] Zhu Q M, Liu Z L, Yan J H. Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks[J]. Computational Mechanics, 67, 619-635(2021).

[92] Chaudhari S, Mithal V, Polatkan G et al. An attentive survey of attention models[J]. ACM Transactions on Intelligent Systems and Technology, 12, 53(2021).

[93] Joshi M, Chen D Q, Liu Y H et al. SpanBERT: improving pre-training by representing and predicting spans[J]. Transactions of the Association for Computational Linguistics, 8, 64-77(2020).

[94] Radford A, Narasimhan K, Salimans T et al. Improving language understanding by generative pre-training[EB/OL]. https:∥gwern.net/doc/www/s3-us-west-2.amazonaws.com/d73-fdc5ffa8627bce44dcda2fc012da638ffb158.pdf

[95] Touvron H, Cord M, Douze M et al. Training data-efficient image transformers & distillation through attention[EB/OL]. https:∥arxiv.org/abs/2012.12877

[96] Dhariwal P, Nichol A. Diffusion models beat GANs on image synthesis[EB/OL]. https:∥arxiv.org/abs/2105.05233

[97] Shrivastava A, Pfister T, Tuzel O et al. Learning from simulated and unsupervised images through adversarial training[C], 2242-2251(2017).

[98] Zhou B L, Khosla A, Lapedriza A et al. Learning deep features for discriminative localization[C], 2921-2929(2016).