Research Progress of Flow Field Analysis and In-situ Monitoring for Large-scale Laser Powder Bed Fusion

doi:10.3901/JME.2023.19.389

Abstract

Abstract: Laser powder bed fusion (LPBF) technology with multi-laser beam splicing has the characteristics of high forming efficiency and large forming size, which can meet the manufacturing requirements of large complex and precise key components in aerospace, nuclear power and other fields, and has become the inevitable trend of LPBF technology development. However, how to ensure the stability, process reproducibility and quality reliability of large-scale LPBF forming process is the key challenge of this technology. In recent years, obtaining uniform and consistent airflow through analysis and optimization of the gas flow field is an effective way to achieve stable and high quality forming of large-scale LPBF for a long time. At the same time, the introduction of in-situ monitoring technology can realize real-time monitoring, feedback and adjustment of the molding process, which is a key technical means to grasp the process regulation rules and solve the process instability. Thus, the research progress in flow field analysis and in-situ monitoring of large-scale LPBF is reviewed: In the analysis of flow field, the influence of flow field on spattering and forming performance is systematically analyzed from three aspects: flow channel structure optimization design, flow field parameters and multi-physical field coupling simulation. In the aspect of in-situ monitoring of forming process, the current research status of in-situ monitoring, data processing and analysis, and process control methods of LPBF process is systematically discussed. Finally, the existing problems and development trend of large-scale LPBF flow field analysis and in-situ monitoring are discussed. A phased summary of the research progress of large-scale LPBF is conducted based on the two key directions of flow field analysis and in-situ monitoring, aiming to provide important references for promoting the development of large-scale LPBF technology.

Key words: multi-laser, large-scale, laser powder bed fusion, flow field analysis, in-situ monitoring

CLC Number:

TG665

YANG Yongqiang, JIANG Renwu, LIU Zixin, ZHOU Hanxiang, LI Yang, WANG Di. Research Progress of Flow Field Analysis and In-situ Monitoring for Large-scale Laser Powder Bed Fusion[J]. Journal of Mechanical Engineering, 2023, 59(19): 389-410.

References

[1] KHORASANI A M,GIBSON I,VEETIL J K,et al. A review of technological improvements in laser-based powder bed fusion of metal printers[J]. The International Journal of Advanced Manufacturing Technology,2020,108(1):191-209.
[2] DEBROY T,WEI H L,ZUBACK J S,et al. Additive manufacturing of metallic components-Process,structure and properties[J]. Progress in Materials Science,2018,92:112-124.
[3] MACDONALD E,WICKER R. Multiprocess 3D printing for increasing component functionality[J]. Science,2016,353(6307):aaf2093.
[4] DELGADO J,CIURANA J,RODRIGUEZ C A. Influence of process parameters on part quality and mechanical properties for DMLS and SLM with iron-based materials[J]. International Journal of Advanced Manufacturing Technology,2012,60(5-8):601-610.
[5] GONG Haijun,RAFI K,GU Hengfeng,et al. Influence of defects on mechanical properties of Ti-6Al-4V components produced by selective laser melting and electron beam melting[J]. Materials & Design,2015,86:545-554.
[6] 尚广庆,梁伟. 金属激光选区熔化(SLM)技术及设备概况[J]. 苏州市职业大学学报,2018,29(3):17-21. SHANG Guagqing,LIANG Wei. Research on the technique and equipment of metal selective laser melting(SLM)[J]. Journal of Suzhou Vocational University,2018,29(3):17-21.
[7] 王泽敏,黄文普,曾晓雁. 激光选区熔化成形装备的发展现状与趋势[J]. 精密成形工程,2019,11(4):21-28. WANG Zemin,HUANG Wenpu,ZENG Xiaoyan. Status and prospect of selictive laser melting machines[J]. Journal of Netshape Forming Engineering,2019,11(4):21-28.
[8] LIU Zixin,YANG Yongqiang,HAN Changjun,et al. Effects of gas flow parameters on droplet spatter features and dynamics during large-scale laser powder bed fusion[J]. Materials & Design,2023,225:111534.
[9] LADEWIG A,SCHLICK G,FISSE M,et al. Influence of the shielding gas flow on the removal of process by-products in the selective laser melting process[J]. Additive Manufacturing,2016,10:1-9.
[10] MORAN T P,WARN D H,SOLTANI-TEHRANI A,et al. Spatial inhomogeneity of build defects across the build plate in laser powder bed fusion[J]. Additive Manufacturing,2021,47,10233.
[11] FERRAR B,MULLEN L,JONES E,et al. Gas flow effects on selective laser melting (SLM) manufacturing performance[J]. Journal of Materials Processing Technology,2012,212(2):355-364.
[12] PHILO A,LAVERY N P,BROWN S G R,et al. Comparison and validation of gas flow models in a powder bed selective laser melting process[C]//Proceedings of the 23rd UK Conference of the Association for Computational Mechanics in Engineering. Swansea:TEXAS,2015,8-10.
[13] PHILO A M,SYTCLIFFE C J,SILLARS S,et al. A Study into the effects of gas flow inlet design of the renishaw AM250 laser powder bed fusion machine using computational modelling[C]//Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium. Austin:TEXAS,2017,1203.
[14] WANG Weicheng,CHANG Chiayao. Flow analysis of the laminated manufacturing system with laser sintering of metal powder. Part I:Flow uniformity inside the working chamber[J]. The International Journal of Advanced Manufacturing Technology,2017,92:1299-1314.
[15] CHEN Xiangxin,TZENG Szjia,WANG Weicheng. Numerical and experimental observations of the flow field inside a selective laser melting (SLM) chamber through computational fluid dynamics (CFD) and particle image velocimetry (PIV)[J]. Powder Technology,2020,362:450-461.
[16] LIU Zixin,YANG Yongqiang,WANG Di,et al. Flow field analysis for multilaser powder bed fusion and the influence of gas flow distribution on parts quality[J]. Rapid Prototyping Journal,2022,28(9):1706-1716.
[17] SLM Slutions. NGX XII 600[EB OL]. https://www.slm-pushing-the-limits.com/specs.
[18] 肖建军,谢洋生. 选区激光熔化金属三维打印设备的风场仿真与优化[J]. 装备机械,2021,176(2):34-39. XIAO Jianjun,XIE Yangsheng. Wind field simulatuion and optimization of 3D metal printing device with SLM[J]. The Magazine on Equipment Machinery,2021,176(2):34-39.
[19] 孙宏睿. SLM气氛循环系统风场仿真优化及打印试验对比[J]. 精密制造与自动化,2020,222(2):5-10. SUN Hongrui. Optimization of wind field simulatuion and comparation of printing test of SLM atmosphere circulation system[J]. Precise Manufacturing & Automation,2020,222(2):5-10.
[20] YANG Yongqiang,CHEN Ziyu,LIU Zixin,et al. Influence of shielding gas flow consistency on parts quality consistency during large-scale laser powder bed fusion[J]. Optics & Laser Technology,2023,158:108899.
[21] ZHANG Xiaobing,CHENG Bo,TUFFILE Charles. Simulation study of the spatter removal process and optimization design of gas flow system in laser powder bed fusion[J]. Additive Manufacturing,2020,32:101049.
[22] CHEN Yu,VASTOLA G,ZHANG Yongwei. Optimization of inert gas flow inside laser powder bed fusion chamber with computational fluid dynamics[C]//29th Annual International Solid Freeform Fabrication Symposium-An Additive Manufacturing Conference. Austin:University of Texas at Austin,2018,1931-1939.
[23] 梁平华,唐倩,余志强,等. 激光选区熔化的风场仿真与流道结构优化[J]. 中国机械工程,2019,30(7):858-863. LIANG Pinghua,TANG Qian,YU Zhiqiang,et al. Gas field simulation and flow channel structure optimization of SLM[J]. China Mechanical Engineering,2019,30(7):858-863.
[24] 谢雪芬,张豪,章诗婷,等. 选区激光熔化气氛保护风场仿真及优化[J]. 制造业自动化,2021,43(12):131-134. XIE Xuefen,ZHANG Hao,ZHANG Shiting,et al. Wind field simulatuion and optimization of shielding gas atmosphere for SLM process[J]. Manufacturing Automation,2021,43(12):131-134.
[25] ANDREAS W,BODO H. Apparatus for Producing Work Pieces under Elevated Pressure:Europe,EP2774703A1[P]. 2014-09-10.
[26] 贾斯汀·曼拉克,麦肯齐·赖安·雷丁. 增材制造机器的气流系统:中国,CN201880072411.8[P]. 2022-08-12. MANLAC J,READING M R. Air flow system of additive manufacturing machine:China,CN201880072411.8[P]. 2022-08-12.
[27] RIVA F,CANAGLIA S. Gas flow within additive manufacturing device:Europe,EP3023228B1[P]. 2018-08-08.
[28] 张正文,刘飞,毛忠发,等. 用于选择性激光熔化的烟尘处理装置:中国,CN201510519496.8[P]. 2017-04-05. ZHANG Zhengwen, LIU Fei,MAO Zhongfa,et al. Smoke and dust treatment device for selective laser melting; China,CN201510519496.8[P]. 2017-04-05.
[29]赵彦辰. 金属3D打印机吹风装置:中国,CN112024879A[P]. 2020-12-04. ZHAO Yanchen. Metal 3D printer hair dryer:China,CN112024879A[P]. 2020-12-04.
[30] 付旺琪,李腾飞,钱波,等. 基于FLUENT的小型激光选区熔化设备气氛保护系统优化设计[J]. 电焊机,2019,49(9):78-83. FU Wanqi,LI Tengfei,QIAN Bo,et al. Optimal design of atmosphere protection system for selective melting device based on FLUENT[J]. Electric Welding Machine,2019,49(9):78-83.
[31] 亢志颖,刘杰,郑泽锦,等. 激光选区熔化气氛保护装置的流场分析及优化设计[J]. 机械设计,2022,39(6):100-107. KANG Zhiying,LIU Jie,ZHENG Zhejin,et al. Analysis and optimization design of flow field of the atmosphere-protection equitment's selective laser melting device[J]. Journal of Machine Design,2022,39(6):100-107.
[32] WANG X J,ZHANG L C,FANG M H,et al. The effect of atmosphere on the structure and properties of a selective laser melted Al-12Si alloy[J]. Materials Science and Engineering:A,2014,597:370-375.
[33] DAI Donghua,GU Dongong. Effect of metal vaporization behavior on keyhole-mode surface morphology of selective laser melted composites using different protective atmospheres[J]. Applied Surface Science,2015,355:310-319.
[34] RALESH C S,PRIYANKA N,JAYAGANTHAN,et al. Effect of build atmosphere on the mechanical properties of AlSi10Mg produced by selective laser melting[J]. Materials Today:Proceedings,2018,5(9):17231-17238.
[35] PAUZON C,FORET P,HRYHA E,et al. Argon-helium mixtures as Laser-Powder Bed Fusion atmospheres:Towards increased build rate of Ti-6Al-4V[J]. Journal of Materials Processing Technology,2020,279:116555.
[36] TRAORE S,SCHNEIDER M,KOUTIRI I et al. Influence of gas atmosphere (Ar or He) on the laser powder bed fusion of a Ni-based alloy[J]. Journal of Materials Processing Technology,Elsevier,2021,288:116851.
[37] TRAORE S,KOUTIRI I,SCHNEIDER M,et al. Influence of gaseous environment on the properties of Inconel 625 L-PBF parts[J]. Journal of Manufacturing Processes,2022,84:1492-1506.
[38] REIJONEN J,REVUELTA A,RIIPINEN T,et al. On the effect of shielding gas flow on porosity and melt pool geometry in laser powder bed fusion additive manufacturing[J]. Additive Manufacturing,2020,32:101030.
[39] CHEN Yanzhe,WEN Peng,VOSHAGE M,et al. Laser additive manufacturing of Zn metal parts for biodegradable implants:Effect of gas flow on evaporation and formation quality[J]. Journal of Laser Applications,2019,31:022304.
[40] WEN Peng,QIN Yu,CHEN Yanzhe,et al. Laser additive manufacturing of Zn porous scaffolds:Shielding gas flow,surface quality and densification[J]. Journal of Materials Science and Technology,2019,35(2):368-376.
[41] ANWAR A B,PHAM Q C. Selective laser melting of AlSi10Mg:Effects of scan direction,part placement and inert gas flow velocity on tensile strength[J]. Journal of Materials Processing Technology,2017,240:388-396.
[42] YANG G,XIE Y,ZHAO S,et al. Quality control:Internal defects formation mechanism of selective laser melting based on laser-powder-melt pool interaction:A review[J]. Chinese Journal of Mechanical Engineering:Additive Manufacturing Frontiers,2022:100037.
[43] ANWAR A B,Ibrahim I H,Pham Q C. Spatter transport by inert gas flow in selective laser melting:A simulation study[J]. Powder technology,2019,352:103-116.
[44] ANWAR A B,Pham Q C. Study of the spatter distribution on the powder bed during selective laser melting[J]. Additive Manufacturing,2018,22:86-97.
[45] BIDARE P,BITHARAS I,WARD R M,et al. Fluid and particle dynamics in laser powder bed fusion[J]. Acta Materialia,2018,142:107-120.
[46] CHEN H,YAN W. Spattering and denudation in laser powder bed fusion process:Multiphase flow modelling[J]. Acta Materialia,2020,196:154-167.
[47] LI X,ZHAO C,SUN T,et al. Revealing transient powder-gas interaction in laser powder bed fusion process through multi-physics modeling and high-speed synchrotron x-ray imaging[J]. Additive Manufacturing,2020,35:101362.
[48] LI Xuxiao,GUO Qilin,Chen Lianyi,et al. Quantitative investigation of gas flow,powder-gas interaction,and powder behavior under different ambient pressure levels in laser powder bed fusion[J]. International Journal of Machine Tools and Manufacture,2021,170:103797.
[49] KLESZCZYNSKI S,ZUR JACOBSMÜHLEN J,Reinarz B,et al. Improving process stability of laser beam melting systems[C]//Fraunhofer Di⁃rect Digital Manufacturing Conference. Berlin,2014,1⁃6.
[50] SEITA M. A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing[J]. Materials & Design,2019,164:107562.
[51] 张鹏. 基于视觉的激光选区熔化成形铺粉质量在线监控系统研究[D]. 武汉:华中科技大学,2017. ZHANG Peng. Research of a motoring system of deposition quality in selective laser melting based on machine vision[D]. Wuhan:Huazhong University of Science&Technology,2017.
[52] 黄阿童,姚山. 基于图像处理的增材制造铺粉缺陷自动检测[J]. 工业控制计算机,2020,33(10):7-10. HUANG Atong,YAO Shan. Automated dection of powder spreading defects in additive manufacturing based on image processing[J]. Industrial Control Computer,2020,33(10):7-10.
[53] LAND II W S,Zhang Bin,ZIEGERT J,et al. In-situ metrology system for laser powder bed fusion additive process[J]. Procedia Manufacturing,2015,1:393-403.
[54] 赵蒙蒙,刘丰,张肖男,等. 基于表面轮廓参数评定的铺粉质量研究[J]. 铸造技术,2018,39(10):2296-2299. ZHAO Mengmeng,LIU Feng,ZHANG Xiaonan,et al. Study on powder laying quality based on the evaluation of surface profile parameters[J]. Foundry Technology,2018,39(10):2296-2299.
[55] ZHANG Bin,ZIEGERT J,DAVIES A.In situ surface metrology of laser powder bed fusion processes using fringe projection[C]//Proc of Joint Special Interest Group Meeting between Euspen and ASPE. Charlotte:Curran Associates Inc,2016.
[56] NEEF A,SEYDA V,HERZOG D,et al. Low coherence interferometry in selective laser melting[J]. Physics Procedia,2014,56:82-89.
[57] GRONDEL S,DELEBARRE C,ASSAAD J,et al. Fatigue crack monitoring of riveted aluminium strap joints by Lamb wave analysis and acoustic emission measurement techniques[J]. NDT & E International,2002,35(3):137-146.
[58] YE Dongsen,HONG Geoksoon,ZHANG Yingjie,et al. Defect detection in selective laser melting technology by acoustic signals with deep belief networks[J]. The International Journal of Advanced Manufacturing Technology,2018,96:2791-2801.
[59] TAKUMA M,HISADA S,SAITOH K,et al. Acoustic emission measurement by fiber Bragg grating glued to cylindrical sensor holder[J]. Advances in Materials Science and Engineering,2014,2014.
[60] SHEVCHILK 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,2018,21:598-604.
[61] TEMPELMAN J R,WACHTOR A J,FLYMN E B,et al. Detection of keyhole pore formations in laser powder-bed fusion using acoustic process monitoring measurements[J]. Additive Manufacturing,2022,55:102735.
[62] WU Haixi,YU Zhonghua,WANG Yan. A new approach for online monitoring of additive manufacturing based on acoustic emission[C]//International Manufacturing Science and Engineering Conference. Blacksburg:American Society of Mechanical Engineers,2016,49910:V003T08A013.
[63] LI Zheng,LI Hao,YIN Jie,et al. A review of spatter in laser powder bed fusion additive manufacturing:In situ detection,generation,effects,and countermeasures[J]. Micromachines,2022,13(8):1366.
[64] LIU Yang,YANG Yongqiang,MAI Shuzhen,et al. Investigation into spatter behavior during selective laser melting of AISI 316L stainless steel powder[J]. Materials & Design,2015,87:797-806.
[65] WANG Di,WU Shibiao,FU Fan,et al. Mechanisms and characteristics of spatter generation in SLM processing and its effect on the properties[J]. Materials & Design,2017,117:121-130.
[66] WANG Di,YE Guangzhao,DOU Wenhao,et al. Influence of spatter particles contamination on densification behavior and tensile properties of CoCrW manufactured by selective laser melting[J]. Optics & Laser Technology,2020,121:105678.
[67] WANG Di,DOU Wenhao,QU Yuanhui,et al. Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion[J]. Journal of Materials Research and Technology,2021,12:1051-1064.
[68] ZHANG Yingjie,FUH J Y H,YE Dongsen,et al. In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches[J]. Additive Manufacturing,2019,25:263-274.
[69] LOTT P,SCHLEIFENBAUM H,MEINERS W,et al. Design of an optical system for the in situ process monitoring of selective laser melting (SLM)[J]. Physics Procedia,2011,12:683-690.
[70] BERUMEN S,BECHMANN F,LINDNER S,et al. Quality control of laser-and powder bed-based Additive Manufacturing (AM) technologies[J]. Physics Procedia,2010,5:617-622.
[71] GOOSSENS L R,VAN H B. A virtual sensing approach for monitoring melt-pool dimensions using high speed coaxial imaging during laser powder bed fusion of metals[J]. Additive Manufacturing,2021,40:101923.
[72] DUNBAR A J,NASSARR A R. Assessment of optical emission analysis for in-process monitoring of powder bed fusion additive manufacturing[J]. Virtual and Physical Prototyping,2018,13(1):14-19.
[73] LOUGH C S,ESCANO L I,QU M,et al. In-Situ Optical Emission Spectroscopy During SLM of 304L Stainless Steel[J]. 2018.
[74] YADROITSEV I,KRAKHMALEV P,YADROITSAVA I. Selective laser melting of Ti6Al4V alloy for biomedical applications:Temperature monitoring and microstructural evolution[J]. Journal of Alloys and Compounds,2014,583:404-409.
[75] HOOPER P A. Melt pool temperature and cooling rates in laser powder bed fusion[J]. Additive Manufacturing,2018,22:548-559.
[76] EGAN D S,JONES K,DPWLING D P. Selective laser melting of Ti-6Al-4V:Comparing μCT with in-situ process monitoring data[J]. CIRP Journal of Manufacturing Science and Technology,2020,31:91-98.
[77] BI Guijun,SCHUMANN B,GASSER A,et al. Development and qualification of a novel laser-cladding head with integrated sensors[J]. International Journal of Machine Tools and Manufacture,2007,47(3-4):555-561.
[78] 袁景光,李宇,刘京南,等. 选区激光熔化金属成型熔池温度的在线检测[J]. 中国激光,2020,47(3):158-166. YUAN Jingguang,LI Yu,LIU Jingnan,et al. Online detection of molten pool temperature during metal forming based on selective laser melting[J]. Chinese Journal of Lasers,2020,47(3):158-166.
[79] EVERTON S K,HIRSCH M,STRAVROULAKIS P,et al. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing[J]. Materials & Design,2016,95:431-445.
[80] ZHANG Wenyou,TONG Mingming,HARRISON N M. Scanning strategies effect on temperature,residual stress and deformation by multi-laser beam powder bed fusion manufacturing[J]. Additive Manufacturing,2020,36:101507.
[81] WEI Kaiwen,LI Fangzhi,HUANG Gao,et al. Multi-laser powder bed fusion of Ti-6Al-4V alloy:defect,microstructure,and mechanical property of overlap region[J]. Materials Science and Engineering:A,2021,802:140644.
[82] LI Zhonghua,KUAI Zezhou,BAI Peikang,et al. Microstructure and tensile properties of AlSi10Mg alloy manufactured by multi-laser beam selective laser melting (SLM)[J]. Metals,2019,9(12):1337.
[83] MASOOMI M,THOMPSON S M,SHAMSAEI N. Quality part production via multi-laser additive manufacturing[J]. Manufacturing Letters,2017,13:15-20.
[84] WONG H,DAWSON K,RAVI G A,et al. Multi-laser powder bed fusion benchmarking-Initial trials with Inconel 625[J]. The International Journal of Advanced Manufacturing Technology,2019,105(7):2891-2906.
[85] XIANG Yu,ZHANG Shuzhe,WEI Zhengying,et al. Forming and defect analysis for single track scanning in selective laser melting of Ti6Al4V[J]. Applied Physics A,2018,124:1-12.
[86] YAN Wentao,GE Wenjun,QIAN Ya,et al. Multi-physics modeling of single/multiple-track defect mechanisms in electron beam selective melting[J]. Acta Materialia,2017,134:324-333.
[87] SCHNELL N,SCHOELER M,WITT G,et al. Experimental and numerical thermal analysis of the laser powder bed fusion process using in situ temperature measurements of geometric primitives[J]. Materials & Design,2021,209:109946.
[88] KHAIRALLAH S A,ANDERSON A T,RUBENCHILK A,et al. Laser powder-bed fusion additive manufacturing:Physics of complex melt flow and formation mechanisms of pores,spatter,and denudation zones[J]. Acta Materialia,2016,108:36-45.
[89] PAVLOV M,DOUBENSKAIA M,SMUROV I. Pyrometric analysis of thermal processes in SLM technology[J]. Physics Procedia,2010,5:523-531.
[90] MAHATO V,OBEIDI M A,BRABAZON D,et al. An evaluation of classification methods for 3d printing time-series data[J]. IFAC-PapersOnLine,2020,53(2):8211-8216.
[91] FORIEN J B,CALTA N P,DEPOND P J,et al. Detecting keyhole pore defects and monitoring process signatures during laser powder bed fusion:A correlation between in situ pyrometry and ex situ X-ray radiography[J]. Additive Manufacturing,2020,35:101336.
[92] 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,2018,49:229-239.
[93] HEIGEL J C,LANE B M. Measurement of the melt pool length during single scan tracks in a commercial laserpowder bed fusion process[J]. Journal of Manufacturing Science and Engineering,2018,140(5):051012.
[94] ZHENG Liping,ZHANG Qi,CAO Hongbo,et al. Melt pool boundary extraction and its width prediction from infrared images in selective laser melting[J]. Materials & Design,2019,183:108110.
[95] GUTKMECHT K,LOOTS M,SOMMERTHUBER R,et al. Mutual comparison of acoustic,pyrometric and thermographic laser powder bed fusion monitoring[J]. Materials & Design,2021,210:110036.
[96] KATAYAMA S,MIZUTANI M,MATSUNAWA A. Development of porosity prevention procedures during laser welding[C]//First International Symposium on High-power Laser Macroprocessing. SPIE,2003,4831:281-288.
[97] CUNNINGHAM R,ZHAO Cang,PARAB N,et al. Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging[J]. Science,2019,363(6429):849-852.
[98] GUO Qilin,ZHAO Cang,QU Minglei,et al. In-situ characterization and quantification of melt pool variation under constant input energy density in laser powder bed fusion additive manufacturing process[J]. Additive Manufacturing,2019,28:600-609.
[99] ZHAO Cang,FEZZAA K,CUNNINGHAM R W,et al. Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction[J]. Scientific reports,2017,7(1):3602.
[100] ZHAO Cang,PARAB N D,LI Xuxiao et al. Critical instability at moving keyhole tip generates porosity in laser melting[J]. Science,2020,370(6520):1080-1086.
[101] RICHTER B,BLANKE N,WERNER C,et al. High-speed X-ray investigation of melt dynamics during continuous-wave laser remelting of selective laser melted Co-Cr alloy[J]. CIRP Annals,2019,68(1):229-232.
[102] 曹龙超,周奇,韩远飞,等. 激光选区熔化增材制造缺陷智能监测与过程控制综述[J]. 航空学报,2021,42(10):199-233. CAO Longchao,ZHOU Qi,HAN Yuanfei,et al. Review on intelligent monitoring of defects and process control of celective laser melting addtive manufacturing[J]. Acta Aeronautica et Astronautica Sinica,2021,42(10):199-233.
[103] GOBERT C,REUTZEL E W,PETRICH J,et al. Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging[J]. Additive Manufacturing,2018,21:517-528.
[104] TAHERKHANI K,ERO O,LIRAVI F,et al. On the application of in-situ monitoring systems and machine learning algorithms for developing quality assurance platforms in laser powder bed fusion:A review[J]. Journal of Manufacturing Processes,2023,99:848-897.
[105] TAPIA G,KHAIRALLAH S,MATTEWS M,et al. Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel[J]. The International Journal of Advanced Manufacturing Technology,2018,94:3591-3603.
[106] YUAN B,GUSS G M,WILSON A C,et al. Machine-learning-based monitoring of laser powder bed fusion[J]. Advanced Materials Technologies,2018,3(12):1800136.
[107] O'LOUGHLIN S,DUTTON B,SEMAJ G,et al. Towards in-process prediction of voids in laser powder bed fusion[J]. JOM,2021,73:3240-3249.
[108] 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,2018,19:114-126.
[109] FATHIZADAN S,JU F,LU Y. Deep representation learning for process variation management in laser powder bed fusion[J]. Additive Manufacturing,2021,42:101961.
[110] TATHERKJANI K,EISCHER C,TOYSHERKANI E. An unsupervised machine learning algorithm for in-situ defect-detection in laser powder-bed fusion[J]. Journal of Manufacturing Processes,2022,81:476-489.
[111] 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,2019,25:151-165.
[112] 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,2019,27:42-53.
[113] KRUTH J P,MERCELICSE P,VAN V J,et al. Virtual and Rapid Manufacturing[M]. Boca Raton:CRC Press,2007.
[114] RENKEN V,VON F A,SCHUNEMANN K,et al. In-process closed-loop control for stabilising the melt pool temperature in selective laser melting[J]. Progress in Additive Manufacturing,2019,4:411-421.
[115] HUANG Xiaokang,TIAN Xiaoyong,ZHONG Qi,et al. Real-time process control of powder bed fusion by monitoring dynamic temperature field[J]. Advances in Manufacturing,2020,8:380-391.
[116] PURTONEN T,KALLIOSAARI A,SALMINEN A. Monitoring and adaptive control of laser processes[J]. Physics Procedia,2014,56:1218-1231.