激光增材制造工艺参数优化建模的研究进展

周妃四; 李时春; 陈曦; 蔡文靖; 欧敏; 周磊; 周妃四; 李时春; 陈曦; 蔡文靖; 欧敏; 周磊

doi:10.7510/jgjs.issn.1001-3806.2023.04.005

[1]

杨永强, 陈杰, 宋长辉, 等. 金属零件激光选区熔化技术的现状及进展[J]. 激光与光电子学进展, 2018, 55(1): 011401.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]

韩宇琛, 周孟源, 李茂源, 等. 激光增材制造工艺参数的研究现状[J]. 模具工业, 2019, 45(9): 1-7.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]

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. doi: 10.1007/s00170-014-6338-x

[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]

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]

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. doi: 10.1016/j.commatsci.2009.04.002

[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. doi: 10.1361/105996306X146785

[8]

UYANIK G K, GüLER N. A study on multiple linear regression analysis[J]. Procedia-Social Behavioral Sciences, 2013, 106: 234-240. doi: 10.1016/j.sbspro.2013.12.027

[9]

刘超. 回归分析: 方法、数据与R的应用[M]. 北京: 高等教育出版社, 2019: 38-45.LIU Ch. Regression analysis: Application of methods, data, and R[M]. Beijing: Higher Education Press, 2019: 38-45(in Chinese).

[10]

向枭, 王敏, 殷鸣, 等. 基于30CrNi2MoVA的激光熔化沉积工艺参数研究[J]. 机械, 2020, 47(5): 33-39.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]

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]

田威, 廖文和, 许波, 等. 基于回归分析的激光熔覆几何特征模型修正[J]. 材料热处理学报, 2012, 33(s1): 110-114.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]

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]

许波, 田威. 面向绿色再制造的单道激光熔覆几何特征研究[J]. 应用激光, 2010, 30(4): 254-258.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).

[15]

孙健峰. 激光选区熔化Ti6Al4V可控多孔结构制备及机理研究[D]. 广州: 华南理工大学, 2013: 72-95.SUN J F. Research on fabrication and forming mechanism of controllable porous structure of Ti6AI4V based on selective laser melting[D]. Guangzhou: South China University of Technology, 2013: 72-95(in Chinese).

[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]

吴道祥, 周杰, 马鹏程, 等. 基于响应面法的7050铝合金筋板类锻件热模锻成形工艺优化[J]. 中南大学学报(自然科学版), 2017, 48(3): 601-607.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]

吉利. 基于响应面法的航天器有限元模型修正方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2020: 8-16.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]

黄梓麟. 叶轮材料激光热丝熔覆工艺优化与质量评价研究[D]. 北京: 北京交通大学, 2020: 19-34.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]

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]

梁万旭, 杨勇, 金康, 等. 基于响应面法的同轴送粉多道激光熔覆层形貌预测[J]. 激光与光电子学进展, 2022, 59(1): 0114012.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]

吴腾, 师文庆, 谢林圯, 等. 激光熔覆铁基TiC复合涂层成形质量的控制方法. 激光技术, 2022, 46(3): 344-354.WU T, SHI W Q, XIE L Y, et al. Forming quality control method of laser cladding Fe-based TiC composite coating. Laser Technology, 2022, 46(3): 344-354(in Chinese).

[23]

OLAKANMI E O, NYADONGO S T, MALIKONGWA K, et al. Multi-variable optimisation of the quality characteristics of fiber-laser cladded Inconel-625 composite coatings[J]. Surface & Coatings Technology, 2019, 357: 289-303.

[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]

王赟达, 杨永强, 宋长辉, 等. 基于响应面法优化激光选区熔化成型CoCrMo合金工艺及其电化学行为[J]. 中国有色金属学报, 2014, 24(10): 2497-2505.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]

鄢然, 李浩, 李军超, 等. 基于响应面法的聚苯乙烯粉末选择性激光烧结成型工艺参数优化[J]. 中国激光, 2019, 46(3): 0302015.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]

许向川. 面向再制造的激光熔覆的工艺参数多目标优化[D]. 太原: 中北大学, 2019: 30-31.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]

张嘉瓴. 基于数据挖掘技术的道路交通事故分析[D]. 大连: 大连理工大学, 2020: 31-33.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]

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]

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]

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]

莫彬. 激光增材制造金刚石砂轮工艺优化研究[D]. 湘潭: 湖南科技大学, 2021: 29-40.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]

谷倩微, 邓朝晖, 吕黎曙, 等. 磨削表面形貌建模研究进展[J]. 宇航材料工艺, 2021, 51(2): 1-10.GU Q W, DENG Zh H, LV L Sh, et al. Research progress of grinding surface topography modeling[J]. Aerospace Materials & Technology, 2021, 51(2): 1-10(in Chinese).

[34]

雷凯云, 秦训鹏, 刘华明, 等. 基于神经网络的宽带激光熔覆熔池特征参数预测[J]. 光电子·激光, 2018, 29(11): 1212-1220.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]

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]

姜淑娟, 刘伟军, 南亮亮. 基于神经网络的激光熔覆高度预测[J]. 机械工程学报, 2009, 45(3): 269-274.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]

刘兆平, 王宏松, 修辉平. 基于BP神经网络的覆膜砂选择性激光烧结件精度预测[J]. 热加工工艺, 2016, 45(21): 91-93.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]

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]

赵凯, 梁旭东, 王炜, 等. 基于NSGA-Ⅱ算法的同轴送粉激光熔覆工艺多目标优化[J]. 中国激光, 2020, 47(1): 0102004.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]

孟庆栋. 基于机器学习的激光熔覆形貌预测与监测研究[D]. 北京: 中国矿业大学, 2020: 16-26.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]

吴天山, 于鸿彬, 李小青, 等. 基于遗传算法的BP神经网络熔融沉积成型翘曲变形预测研究[J]. 热加工工艺, 2019, 48 (22): 48-52.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]

王东生, 杨友文, 田宗军, 等. 基于神经网络和遗传算法的激光多层熔覆厚纳米陶瓷涂层工艺优化[J]. 中国激光, 2013, 40(9): 0903001.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]

肖亚宁, 郭艳玲, 张亚鹏, 等. 基于SOA-BP神经网络的SLS成型件精度预测模型[J]. 科学技术与工程, 2021, 21(23): 9864-9870.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]

武国朋. 基于机器学习的集宁浅覆盖区钼多金属矿成矿预测与评价[D]. 北京: 中国地质大学(北京), 2020: 42-44.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]

ZOUHRI W, DANTAN J Y, HÄFNER 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]

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]

朱传敏, 顾鹏, 刘丁豪, 等. 基于支持向量机的铝基碳化硅磨削表面质量预测[J]. 表面技术, 2019, 48(3): 240-248.ZHU Ch M, GU P, LIU D H, et al. Surface quality prediction of SiC_p/Al composite in grinding based on support vector machine[J]. Surface Technology, 2019, 48(3): 240-248(in Chinese).

[48]

夏田, 郭建斌, 赵一号. 基于改进支持向量机的选区激光熔化参数优化的研究[J]. 热加工工艺, 2021, 50(4): 29-31.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]

曹扬晨, 朱国胜, 祁小云, 等. 基于随机森林的入侵检测分类研究[J]. 计算机科学, 2021, 48(s1): 459-463.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]

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]

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]

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]

梁旭东, 王炜, 赵凯, 等. 随机森林回归分析在激光熔覆形貌预测中的应用[J]. 中国有色金属学报, 2020, 30(7): 1644-1652.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]

葛继科, 邱玉辉, 吴春明, 等. 遗传算法研究综述[J]. 计算机应用研究, 2008, 25(10): 2911-2916.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]

刘帅. AZ61镁合金选择性激光熔化工艺与性能研究[D]. 北京: 北京科技大学, 2020: 126-128.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]

梁永勤, 毕凤荣, 石纯放. 基于遗传算法的麦弗逊悬架参数优化研究[J]. 机械设计, 2017, 34(1): 15-19.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]

林惠乐. 基于遗传神经网络的CO_2弧焊机器人工艺参数优化研究[D]. 南宁: 广西大学, 2015: 30-40.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]

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]

贾莉, 吴龙. 影响激光选区熔化3D打印质量的工艺参数优化研究[J]. 激光杂志, 2021, 42(5): 166-170.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]

魏建锋. 镍基高温合金SLM成形质量研究及工艺优化[D]. 无锡: 江南大学, 2020: 55-57.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]

马志林, 高梦迪, 王庆阳, 等. 基于节能的增材制造工艺参数优化方法研究[J]. 邵阳学院学报(自然科学版), 2021, 18(3): 32-43.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]

郑金兴. 粒子群优化人工神经网络在高速铣削力建模中的应用[J]. 计算机集成制造系统, 2008(9): 1710-1716.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]

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]

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]

倪立斌. 激光熔覆工艺参数优化及扫描路径规划研究[D]. 长沙: 湖南大学, 2011: 33-34.NI L B. Study of the process optimization and scan path in laser cladding[D]. Changsha: Hunan University, 2011: 33-34(in Chinese).

[66]

周家林, 段正澄, 邓建春, 等. 基于粒子群算法的神经网络优化及其在镗孔加工中的应用[J]. 中国机械工程, 2004, 15(21): 49-51.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]

倪立斌, 刘继常, 伍耀庭, 等. 基于神经网络和粒子群算法的激光熔覆工艺优化[J]. 中国激光, 2011, 38(2): 0203003.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]

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]

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]

韩兴国, 宋小辉, 殷鸣, 等. 熔融沉积式3D打印路径优化算法研究[J]. 农业机械学报, 2018, 49(3): 393-401.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]

刘傲. 激光熔覆过程低碳建模与工艺参数优化[D]. 沈阳: 沈阳工业大学, 2021: 53-61.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]

李慧贤, 马创新, 王硕, 等. 金属增材制造负载均衡异构并行切片算法[J]. 中国机械工程, 2021, 32(9): 1102-1107.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]

肖亚宁, 孙雪, 张亚鹏, 等. 基于SOA-LSSVM的SLS成形工艺参数优化研究[J]. 机床与液压, 2022, 50(6): 36-42.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]

张轶伦, 牛艺萌, 叶天竺, 等. 新信息技术下制造服务融合及产品服务系统研究综述[J]. 中国机械工程, 2018, 29(18): 2164-2176.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]

程颖, 戚庆林, 陶飞. 新一代信息技术驱动的制造服务管理: 研究现状与展望[J]. 中国机械工程, 2018, 29(18): 2177-2188.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]

刘涛, 邓朝晖, 葛智光, 等. 面向凸轮轴磨削加工的智能决策云服务实现[J]. 中国机械工程, 2020, 31(7): 773-780.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]

邵建军. 基于神经网络和遗传算法的激光选区熔化成形工艺优化研究[D]. 武汉: 华中科技大学, 2018: 48-58.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]

杜亮. 基于神经网络和遗传算法的选区激光熔化工艺优化研究[D]. 厦门: 厦门理工学院, 2021: 47-52.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).