Laser Technology, Volume. 47, Issue 4, 469(2023)
Research progress in modeling the optimization of process parameters of laser additive manufacturing
[1] [1] YANG Y Q,CHEN J,SONG Ch H,et al. Current status and progress on technology of selective laser melting of metal parts[J]. Laser & Optoelectronics Progress,2018, 55(1): 011401(in Chinese).
[2] [2] HAN Y Ch,ZHOU M Y,LI M Y,et al. Research status of process parameters of laser additive manufacturing[J]. Die & Mould Industry,2019, 45(9): 1-7(in Chinese).
[3] [3] MARZBAN J, GHASEMINEJAD P, AHMADZADEH M H, et al. Experimental investigation and statistical optimization of laser surface cladding parameters[J]. The International Journal of Advanced Manufacturing Technology, 2015, 76 (5-8):1163-1172.
[4] [4] QI H, AZER M, SINGH P. Adaptive toolpath deposition method for laser net shape manufacturing and repair of turbine compressor airfoils[J]. International Journal of Advanced Manufacturing Technology, 2009, 48 (1): 121-131.
[5] [5] ONWUBOLU G C, DAVIM J P, OLIVEIRA C, et al. Prediction of clad angle in laser cladding by powder using response surface methodology and scatter search[J]. Optics & Laser Technology, 2007, 39(6): 1130-1134.
[6] [6] KUMAR A, ROY S. Effect of three-dimensional melt pool convection on process characteristics during laser cladding[J]. Computational Materials Science, 2009, 46(2): 495-506.
[7] [7] DUBOURG L,ST-GEORGES L. Optimization of laser cladding process using taguchi and EM methods for MMC coating production[J]. Journal of Thermal Spray Technology, 2006, 15(4): 790-795.
[8] [8] UYANIK G K, GüLER N. A study on multiple linear regression analysis[J]. Procedia-Social Behavioral Sciences, 2013, 106: 234-240.
[9] [9] LIU Ch. Regression analysis: Application of methods, data, and R[M].Beijing: Higher Education Press,2019: 38-45(in Chinese).
[10] [10] XIANG X,WANG M,YIN M,et al.Process parameters of laser melting deposition based on 30 CrNi2MoVA[J]. Machinery, 2020, 47(5): 33-39(in Chinese).
[11] [11] FAN P, ZHANG G. Study on process optimization of WC-Co50 cermet composite coating by laser cladding[J]. International Journal of Refractory Metals Hard Materials, 2019, 87: 105133.
[12] [12] TIAN W,LIAO W H,XU B,et al.Revision of geometrical feature model of laser cladding based on regressive analyses[J]. Transactions of Materials and Heat Treatment, 2012, 33(s1): 110-114(in Chinese).
[13] [13] DAVIM J P, OLIVEIRA C, CARDOSO A. Predicting the geometric form of clad in laser cladding by powder using multiple regression analysis (MRA)[J]. Materials & Design, 2008, 29(2):554-557.
[14] [14] XU B,TIAN W. The geometrical features of single laser cladding for the green remanufacturing[J]. Applied Laser, 2010, 30(4): 254-258(in Chinese).
[16] [16] KHORRAM A, JAMALOEI A D, PAIDAR M, et al. Laser cladding of Inconel 718 with 75Cr3C2+25(80Ni20Cr) powder: Statistical modeling and optimization[J]. Surface & Coatings Technology, 2019, 378:124933.
[17] [17] WU D X,ZHOU J,MA P Ch,et al.Optimization of hot die forging process parameters of 7050 aluminum alloy rib-web type components based on response surface method[J]. Journal of Central South University (Science and Technology Edition), 2017, 48(3): 601-607(in Chinese).
[18] [18] JI L.Study of finite element model updatingmethod for spacecraft based onresponse surface method[D]. Harbin: Harbin Institute of Technology, 2020:8-16(in Chinese).
[19] [19] HUANG Z L. The process optimization and quality assessment on laser hot wire cladding for the impeller material[D]. Beijing: Beijing Jiaotong University, 2020:19-34(in Chinese).
[20] [20] LIU S, KOVACEVIC R. Statistical analysis and optimization of processing parameters in high-power direct diode laser cladding[J]. International Journal of Advanced Manufacturing Technology, 2014, 74(5/8): 867-878.
[21] [21] LIANG W X,YANG Y,JIN K,et al. Morphology prediction of coaxial pow der feeding multichannel laser clad ding layer based on response surface[J]. Laser & Optoelectronics Progress, 2022, 59(1): 0114012(in Chinese).
[22] [22] WU T, SHI W Q, XIE L Y, et al. Forming quality control method of laser cladding Fe-based TiC composite coating[J].Laser Technology,2022,46(3): 344-354(in Chinese).
[24] [24] FARAHMAND P, KOVACEVIC R. Parametric study and multi-criteria optimization in laser cladding by a high power direct diode laser[J]. Lasers in Manufacturing and Materials Processing, 2014, 1(1/4):1-20.
[25] [25] WANG Y D,YANG Y Q,SONG Ch H,et al. Process optimization and electrochemical behavior of CoCrMo alloy fabricated by selective laser melting based onresponse surface method[J]. The Chinese Journal of Nonferrous Metals, 2014, 24(10): 2497-2505(in Chinese).
[26] [26] YAN R,LI H,LI J Ch, et al. Process parameters optimization of polystyrene powder selective laser sintering based on response surface methodology[J]. Chinese Journal of Lasers, 2019, 46(3): 0302015(in Chinese).
[27] [27] XU X Ch. Multi-objective optimization of laser cladding process parameters for remanufacturing[D]. Taiyuan: North University of China, 2019:30-31(in Chinese).
[28] [28] ZHANG J L. Analysis of road traffic accidents based on data mining technology[D]. Dalian: Dalian University of Technology, 2020:31-33(in Chinese).
[29] [29] KONG Y, BA D Ch, SONG Q Zh. Analysis of process parameters about metal laser melting deposition process of TiAI6V4 alloys based on logistic regression model[J]. VACUUM, 2018, 55(3): 34-40.
[30] [30] LI Sh Ch, MO B, XU W, et al. Research on nonlinear prediction model of weld forming quality during hot-wire laser welding[J]. Optics & Laser Technology, 2020, 131: 106436.
[31] [31] LI Sh Ch, MO B, WANG K M, et al. Nonlinear prediction modeling of surface quality during laser powder bed fusion of mixed powder of diamond and Ni-Cr alloy based on residual analysis[J]. Optics & Laser Technology, 2022, 151: 107980.
[32] [32] MO B.Study on process optimization of diamond grinding wheel by laser additive manufacturing[D]. Xiangtan: Hunan University of Science and Technology, 2021:29-40(in Chinese).
[33] [33] GU Q W, DENG Zh H, L L Sh,et al. Research progress of grinding surface topography modeling[J]. Aerospace Materials & Technology, 2021, 51(2): 1-10(in Chinese).
[34] [34] LEI K Y, QIN X P, LIU H M,et al. Prediction on characteristics of molten pool in wide-band laser cladding based on neural network[J]. Journal of Optoelectronics·Laser, 2018, 29(11): 1212-1220(in Chinese).
[35] [35] QI X, CHEN G, LI Y, et al. Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives[J]. Engineering, 2019, 5(4):721-729.
[36] [36] JIANG Sh J,LIU W J,NAN L L. Laser cladding height prediction based on neural network[J]. Journal of Mechanical Engineering, 2009, 45(3): 269-274(in Chinese).
[37] [37] LIU Zh P,WANG H S,XIU H P. Precision prediction for SLS of resin coated sand based on BP nneural network[J]. Hot Working Technology, 2016, 45(21): 91-93(in Chinese).
[38] [38] CAIAZZO F, CAGGIANO A. Laser direct metal deposition of 2024 Al alloy: Trace geometry prediction via machine learning[J]. Materials & Design, 2018, 11(3):444-455.
[39] [39] ZHAO K,LIANG X D,WANG W,et al. Multi-objective optimization of coaxial powder feeding laser cladding based on NSGA-Ⅱ[J]. Chinese Journal of Lasers, 2020, 47(1): 0102004(in Chinese).
[40] [40] MENG Q D. Research on prediction and monitoring oflaser cladding morphology based on machine learning[D]. Beijing: China University of Mining and Technology, 2020:16-26(in Chinese).
[41] [41] WU T Sh,YU H B,LI X Q,et al. Study on warp deformation prediction in FDM process based on genetic algorithm and BP neural network[J]. Hot Working Technology,2019, 48(22): 48-52(in Chinese).
[42] [42] WANG D Sh,YANG Y W,TIAN Z J,et al. Process optimization of thick nanostructured ceramic coating by laser multi-layer cladding based on neural network and genetic algorithm[J]. Chinese Journal of Lasers, 2013, 40(9): 0903001(in Chinese).
[43] [43] XIAO Y N,GUO Y L,ZHANG Y P,et al. Accuracy predictive model of selective laser sintering based on SOA-BP neural network[J]. Science Technology and Engineering,2021,21(23): 9864-9870(in Chinese).
[44] [44] WU G P. Mapping mineral prospectivity for molybdenumpolymetallic mineralization by machine learning methods in jining, inner mongolia, China[D]. Beijing: China University of Geosciences (Beijing), 2020:42-44(in Chinese).
[45] [45] ZOUHRI W, DANTAN J Y, HFNER B,et al. Characterization of laser powder bed fusion (L-PBF) process quality: A novel approach based on statistical features extraction and support vector machine[J]. Procedia CIRP, 2021, 99:319-324.
[46] [46] CHEN T, WU W N, LI W P,et al. Laser cladding of nanoparticle TiC ceramic powder: Effects of process parameters on the quality characteristics of the coatings and its prediction model[J]. Optics and Laser Technology, 2019, 116: 345-355.
[47] [47] ZHU Ch M,GU P,LIU D H,et al. Surface quality prediction of SiCp/Al composite in grinding based on support vector machine [J]. Surface Technology, 2019, 48(3): 240-248(in Chinese).
[48] [48] XIA T,GUO J B,ZHAO Y H. Research on selective laser melting parameter optimization based on improved support vector machine[J]. Hot Working Technology, 2021, 50(4): 29-31(in Chinese).
[49] [49] CAO Y Ch,ZHU G Sh,QI X Y,et al. Research on intrusion detection classification based on random forest[J].Computer Science, 2021, 48(s1): 459-463(in Chinese).
[50] [50] NGUYEN H, BUI X N. Predicting blast-Induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest[J]. Natural Resources Research, 2018,28:893-907.
[51] [51] ZHU X W, XIN Y J, GE H L. Recursive random forests enable better predictive performance and model interpretation than variable selection by LASSO[J].Journal of Chemical Information and Modeling, 2015,55(4): 736-746.
[52] [52] SMITH P F, GANESH S, LIU P. A comparison of random forest regression and multiple linear regression for prediction in neuroscience[J]. Neurosci Methods, 2013, 220(1): 85-91.
[53] [53] LIANG X D,WANG W,ZHAO K,et al. Application of random forest regression analysis in trace geometry prediction of laser cladding[J]. The Chinese Journal of Nonferrous Metals, 2020, 30(7): 1644-1652(in Chinese).
[54] [54] GE J K,QIU Y H,WU Ch M,et al. Summary of genetic algorithms research[J]. Application Research of Computers, 2008,25(10): 2911-2916(in Chinese).
[55] [55] LIU Sh. Research on the process and properties of AZ61 magnesium alloy fabricated by selective laser melting[D]. Beijing: University of Science and Technology Beijing, 2020:126-128(in Chinese).
[56] [56] LIANG Y Q,BI F R,SHI Ch F. Parametric optimization research for MacPherson suspension based on genetic algorithm[J]. Journal of Machine Design,2017, 34(1): 15-19(in Chinese).
[57] [57] LIN H L.Research on the optimizing welding parameters of CO2 arc welding robot based on genetic neural network[D]. Nanning: Guangxi University, 2015:30-40(in Chinese).
[58] [58] MONDAL S, TUDU B, ASISH B, et al. Process optimization for laser cladding operation of alloy steel using genetic algorithm and artificial neural network[J]. International Journal of Computational Engineering Research, 2012,2(1):18-25.
[59] [59] JIA L,WU L. Study on optimisation of process parameters that affect thequality of 3D printing with laser melting selection[J]. Laser Journal, 2021, 42(5): 166-170(in Chinese).
[60] [60] WEI J F. Research on SLM forming quality and process optimization of nickel-based superalloy[D]. Wuxi: Jiangnan University, 2020:55-57(in Chinese).
[61] [61] MA Zh L,GAO M D,WANG Q Y,et al. Research on optimization method of additive manufacturing process parameters based on energy saving[J]. Journal of Shaoyang University( Natural Science Edition), 2021, 18(3): 32-43(in Chinese).
[62] [62] ZHENG J X. Application of particle-swarm-optimization-trained artificial neural network in high speed milling force modeling[J]. Computer Integrated Manufacturing Systems, 2008(9): 1710-1716(in Chinese).
[63] [63] ZHANG J R, ZHANG J, LOK T M, et al. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training[J]. Applied Mathematics Computation, 2007, 185 (2): 1026-1037.
[64] [64] ZHOU J L, DUAN Zh Ch,LI Y, et al. PSO-based neural network optimization and its utilization in a boring machine[J]. Journal of Materials Processing Technology, 2006, 178(1/3): 19-23.
[65] [65] NI L B. Study of the process optimization and scan path in laser cladding[D]. Changsha: Hunan University, 2011:33-34(in Chinese).
[66] [66] ZHOU J L,DUAN Zh Ch,DENG J Ch,et al. ANN trained by particle swarm optimization and its applications in boring processes[J]. China Mechanical Engineering, 2004,15(21): 49-51(in Chinese).
[67] [67] NI L B,LIU J Ch,WU Y T,et al. Optimization of laser cladding process variables based on neural network and particle swarm optimization algorithms[J]. Chinese Journal of Lasers, 2011, 38(2): 0203003(in Chinese).
[68] [68] MA M Y, XIONG W J, LIAN Y, et al. Modeling and optimization for laser cladding via multi-objective quantum-behaved particle swarm optimization algorithm[J]. Surface Coatings Technology, 2020, 381:125129.
[69] [69] VASUDEVAN M, MURUGANANTH M, BHADURI A K, et al. Bayesian neural network analysis of ferrite number in stainless steel welds[J]. Science and Technology of Welding and Joining, 2004, 9(2):109-120.
[70] [70] HAN X G,SONG X H,YIN M,et al. Path optimization algorithm of 3D printing based on fused deposition modeling[J]. Transactions of the Chinese Society for Agricultural Machinery,2018, 49(3): 393-401(in Chinese).
[71] [71] LIU A. Low-carbon modeling and process parameter optimization in laser additive manufacturing process[D]. Shenyang: Shenyang University of Technology, 2021:53-61(in Chinese).
[72] [72] LI H X,MA Ch X,WANG Sh,et al. Load balancing heterogeneous parallel slice algorithm for metal additive manufacturing[J]. China Mechanical Engineering, 2021, 32(9): 1102-1107(in Chinese).
[73] [73] XIAO Y N,SUN X,ZHANG Y P,et al. Research on optimization of SLS forming processing parameters based on SOA-LSSVM[J]. Machine Tool & Hydraulics, 2022, 50(6): 36-42(in Chinese).
[74] [74] ZHANG Y L,NIU Y M,YE T Zh,et al. A review of researches of manufacturing-service integration and PSS with new ICT[J]. China Mechanical Engineering, 2018, 29(18): 2164-2176(in Chinese).
[75] [75] CHENG Y,QI Q L,TAO F. New IT-driven manufacturing service management: Research status and prospect[J]. China Mechanical Engineering, 2018, 29(18): 2177-2188(in Chinese).
[76] [76] LIU T,DENG Zh H,GE Zh G,et al. Implementation of intelligent decision cloud service for camshaft grinding processes[J]. China Mechanical Engineering, 2020, 31(7): 773-780(in Chinese).
[77] [77] SHAO J J. Research on optimization of selective laser melting processing based on neural network and genetic algorithm[D]. Wuhan: Huazhong University of Science and Technology, 2018:48-58(in Chinese).
[78] [78] DU L. Optimization of selective laser melting process based on neural network and genetic algorithm[D]. Xiamen: Xiamen University of Technology, 2021:47-52(in Chinese).
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ZHOU Feisi, LI Shichun, CHEN Xi, CAI Wenjing, OU Min, ZHOU Lei. Research progress in modeling the optimization of process parameters of laser additive manufacturing[J]. Laser Technology, 2023, 47(4): 469
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Received: Jun. 6, 2022
Accepted: --
Published Online: Dec. 11, 2023
The Author Email: LI Shichun (li.shi.chun@163.com)