Research progress in modeling the optimization of process parameters of laser additive manufacturing
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摘要: 激光增材制造过程的工艺参数直接影响成型件的成形质量及性能，对工艺参数的优化是实现成形质量调控的最有效方法。建立准确、高精度的工艺参数与成形质量之间的模型，对于成形质量的预测及工艺参数的优选极其重要。对激光增材制造工艺参数优化建模的方法进行了总结和综述，且对工艺系统开发进行了现状分析，论述了工艺优化建模方法的原理以及优缺点，最后对激光增材制造工艺优化建模研究前景进行了展望。Abstract: The formation quality and performance of the forming parts are directly affected by the process parameters of laser additive manufacturing. Thus optimizing the process parameters is the most effective way to achieve forming quality regulation. For the prediction of forming quality and the optimization of process parameters, it is extremely important to establish a model describing the relationships between accurate high-precision process parameters and formation quality. In this paper, the method of modeling the optimization of process parameters of laser additive manufacturing were summarized and reviewed, the development of the status quo of process system was also analyzed. The principles, advantages, and disadvantages of the method have been discussed for modeling the optimization of process parameters. Finally, the laser additive manufacturing process optimization modeling research has been prospected.
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Keywords:
- laser technique /
- optimization model /
- process parameters /
- process system /
- additive manufacturing
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图 4 a—支持向量机原理[44] b—改进前后熔池宽预测相对误差对比[48] c—改进前后熔池高预测相对误差对比[48] d—改进前后熔池深预测相对误差对比[48]
Figure 4. a—the principle of support vector machine[44] b—the comparison of relative errors of prediction of melt pool width before and after improvement[48] c—the comparison of relative errors of prediction of melt pool height before and after improvement[48] d—the comparison of relative errors of prediction of melt pool depth before and after improvement[48]
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[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201801002.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201801002.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-MJGY201909006.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-MJGY201909006.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-MECH202005006.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-MECH202005006.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JSCL2012S1025.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JSCL2012S1025.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-YYJG201004002.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-YYJG201004002.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201703007.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201703007.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202201033.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202201033.htm
[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. DOI: 10.7510/jgjs.issn.1001-3806.2022.03.008 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). DOI: 10.7510/jgjs.issn.1001-3806.2022.03.008
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXZ201410009.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXZ201410009.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201903017.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201903017.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-YHCG202102002.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-YHCG202102002.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ201811011.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ201811011.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB200903045.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB200903045.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-SJGY201621026.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-SJGY201621026.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ202001012.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ202001012.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-SJGY201922014.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-SJGY201922014.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201309011.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201309011.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS202123027.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS202123027.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-BMJS201903035.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-BMJS201903035.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-SJGY202104007.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-SJGY202104007.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2021S1081.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA2021S1081.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXZ202007018.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXZ202007018.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ200810009.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ200810009.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JXSJ201701003.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JXSJ201701003.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202105034.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202105034.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXZ202103005.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-SYXZ202103005.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ200809007.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ200809007.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX200421012.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX200421012.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201102020.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201102020.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-NYJX201803050.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-NYJX201803050.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX202109014.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX202109014.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-JCYY202206006.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-JCYY202206006.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201818003.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201818003.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201818004.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201818004.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX202007005.htm 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). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX202007005.htm
[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).
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