激光粉末床熔融增材制造过程智能监控研究进展与挑战

doi:10.3901/JME.2023.19.253

摘要/Abstract

摘要： 激光粉末床熔融(Laser powder bed fusion, LPBF)增材制造逐渐成为难加工金属构件快速、低成本、高性能、短周期制造的“潜力股”，被认为是使用最为广泛的金属增材制造技术之一，已经在航空、航天等工业领域得到大面积应用。然而，增材制造过程与成形质量的稳定一致性是行业面临的挑战性难题，已经成为LPBF增材制造技术迈向规模生产的“拦路虎”。目前的LPBF增材制造监控系统主要在“测”，即实现各类过程信息的测量，其质量评判与调控技术成熟度不够，难以实现行之有效的过程监控，而结合先进传感技术以及人工智能方法的智能监控有望成为LPBF增材制造规模生产的“一把利刃”。从LPBF缺陷类型、制造过程信息感知、过程质量智能评判、工艺参数优化与质量调控四个方面综述激光粉末床熔融增材制造智能监控研究进展和发展现状，指出发展面向LPBF增材制造规模生产的成熟智能监控系统面临的挑战。最后讨论应对这些挑战的解决途径和未来展望。

关键词: 增材制造, 激光粉末床熔融, 质量评判, 智能监控

Abstract: Laser powder bed fusion (LPBF) has gradually become a "potential stock" for fast, low-cost, high-performance, and short-cycle manufacturing of difficult-to-machine metal components. It is considered to be one of the most widely used metal additive manufacturing technologies, and has been widely used in aviation, aerospace and other industrial fields. However, the stability and consistency of additive manufacturing process and forming quality is a challenging problem for the industry and has become a "blocker" for LPBF to embrace the mass production. The current LPBF additive manufacturing monitoring system mainly focuses on "measuring", that is, measuring of various process information. Its quality evaluation and control technology are not mature enough, and intelligent monitoring combined with advanced sensing technology and artificial intelligence methods is expected to become a "sharp edge" for mass production of LPBF additive manufacturing. Following the trend, this paper summarizes the research progress and development status of intelligent monitoring in LPBF additive manufacturing from four aspects：LPBF defect types, process information perception, process quality intelligent evaluation, process parameter optimization and quality control. Some challenges in developing a mature intelligent monitoring system for mass production of LPBF additive manufacturing are pointed out. Finally, solutions and future prospects for addressing these challenges are discussed.

Key words: additive manufacturing, laser powder bed fusion, quality evaluation, intelligent monitoring

中图分类号:

TP277

赵志斌, 王晨希, 张兴武, 陈雪峰, 李应红. 激光粉末床熔融增材制造过程智能监控研究进展与挑战[J]. 机械工程学报, 2023, 59(19): 253-276.

ZHAO Zhibin, WANG Chenxi, ZHANG Xingwu, CHEN Xuefeng, LI Yinghong. Research Progress and Challenges in Process Intelligent Monitoring of Laser Powder Bed Fusion Additive Manufacturing[J]. Journal of Mechanical Engineering, 2023, 59(19): 253-276.

参考文献

[1] 卢秉恒. 增材制造技术-现状与未来[J]. 中国机械工程,2020,31(1):19-23. LU Bingheng. Additive manufacturing:Current situation and future[J]. China Mechanical Engineering,2020,31(1):19-23.
[2] 李涤尘,贺健康,田小永,等. 增材制造:实现宏微结构一体化制造[J]. 机械工程学报,2013,49(6):129-135. LI Dichen,HE Jiankang,TIAN Xiaoyong,et al. Additive manufacturing:Integrated fabrication of macro/microstructures[J]. Journal of Mechanical Engineering,2013,49(6):129-135.
[3] 冯云昊,曹迪,吴金希. 中美典型产业共性技术研究机构比较分析——以两个增材制造研究院为例[J]. 中国科技论坛,2022,311(3):166-175. FENG Yunhao,CAO Di,WU Jinxi. Comparative study on generic technology research institutions in China and the United States:Take two additive manufacturing research institutes as examples[J]. Forum on Science and Technology in China,2022,311(3):166-175.
[4] 汤海波,吴宇,张述泉,等. 高性能大型金属构件激光增材制造技术研究现状与发展趋势[J]. 精密成形工程,2019,11(4):58-63. TANG Haibo,WU Yu,ZHANG Shuquan,et al. Research status and development trend of high performance large metallic components by laser additive manufacturing technique[J]. Journal of Netshape Forming Engineering,2019,11(4):58-63.
[5] 顾冬冬,张红梅,陈洪宇,等. 航空航天高性能金属材料构件激光增材制造[J]. 中国激光,2020,47(5):32-55. GU Dongdong,ZHANG Hongmei,CHEN Hongyu,et al. Laser additive manufacturing of high-performance metallic aerospace components[J]. Chinese Journal of Lasers,2020,47(5):32-55.
[6] 王向明,崔灿,苏亚东,等. 飞机高能束增材制造结构研究[J]. 航空制造技术,2017,529(10):16-21. WANG Xiangming,CUI Can,SU Yadong,et al. Aircraft structures technology based on power beam additive manufacturingaircraft structures technology based on power beam additive manufacturing[J]. Aeronautical Manufacturing Technology,2017,529(10):16-21.
[7] 李中伟,张禹泽,钟凯,等. 激光粉末床熔融光学原位监测技术综述[J]. 华中科技大学学报,2022,50(12):1-9. LI Zhongwei,ZHANG Yuze,ZHONG Kai,et al. In-situ monitoring techniques for laser powder bed fusion additive manufacturing:A review[J]. Journal of Huazhong University of Science and Technology,2022,50(12):1-9.
[8] 王华明. 高性能大型金属构件激光增材制造:若干材料基础问题[J]. 航空学报,2014,35(10):2690-2698. WANG Huaming. Materials' fundamental issues of laser additive manufacturing for high-performance large metallic components[J]. Acta Aeronautica et Astronautica Sinica,2014,35(10):2690-2698.
[9] ALONDRA N,KEI K. National strategy for advanced manufacturing[R]. New York:National Science and Technology Council,2022.
[10] German Institute for Standardization. Additive. manufacturing-Requirements for quality-assured processes at additive manufacturing centres[S]. Berlin:DIN SPEC (PAS),2019
[11] 欧阳安. 增材制造从产业培育步入推广应用新阶段——《增材制造产业发展行动计划(2017-2020年)》解析[J].中国机械工程,2018,29(23):2895-2897. OUYANG An. Additive manufacturing enters a new stage of promotion and application from industrial cultivation:Analysis of the action plan for the development of additive manufacturing industry (2017-2020)[J]. China Mechanical Engineering,2018,29(23):2895-2897.
[12] WANG C,TAN X P,TOR S B,et al. Machine learning in additive manufacturing:State-of-the-art and perspectives[J]. Additive Manufacturing,2020,36:101538.
[13] TANG Y,RAHMANI DEHAGHANI M,WANG G G. Review of transfer learning in modeling additive manufacturing processes[J]. Additive Manufacturing,2023,61:103357.
[14] QIN J,HU F,LIU Y,et al. Research and application of machine learning for additive manufacturing[J]. Additive Manufacturing,2022,52:102691.
[15] RAO P K,LIU J P,ROBERSON D,et al. Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors[J]. Journal of Manufacturing Science and Engineering,2015,137(6):1-12.
[16] WANG P,YANG Y,MOGHADDAM N S. Process modeling in laser powder bed fusion towards defect detection and quality control via machine learning:The state-of-the-art and research challenges[J]. Journal of Manufacturing Processes,2022,73:961-984.
[17] ABOUELNOUR Y,GUPTA N. In-situ monitoring of sub-surface and internal defects in additive manufacturing:A review[J]. Materials & Design,2022,222:111063.
[18] MAHDI J,ERIN L,DOUG W,et al. Strategic guide:Additive manufacturing in-situ technology readiness report[R]. Washington,DC,USA:ASTM International,2023.
[19] PANWISAWAS C,TANG Y T,REED R C. Metal 3D printing as a disruptive technology for superalloys[J]. Nature Communications,2020,11(1):2327.
[20] 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.
[21] 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,2018,24:273-286.
[22] 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,2020,36:101453.
[23] FISCHER F G,ZIMMERMANN M G,PRAETZSCH N,et al. Monitoring of the powder bed quality in metal additive manufacturing using deep transfer learning[J]. Materials & Design,2022,222:111029.
[24] GUO Q,ZHAO C,ESCANO L I,et al. Transient dynamics of powder spattering in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-energy X-ray imaging[J]. Acta Materialia,2018,151:169-180.
[25] ZHAO C,GUO Q,LI X,et al. Bulk-explosion-induced metal spattering during laser processing[J]. Physical Review X,2019,9(2):21052.
[26] YE D,HONG G S,ZHANG Y,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(5):2791-2801.
[27] MOSTAFAEI A,ZHAO C,HE Y,et al. Defects and anomalies in powder bed fusion metal additive manufacturing[J]. Current Opinion in Solid State and Materials Science,2022,26(2):100974.
[28] GORDON J V,NARRA S P,CUNNINGHAM R W,et al. Defect structure process maps for laser powder bed fusion additive manufacturing[J]. Additive Manufacturing,2020,36:101552.
[29] MADISON J D,AAGESEN L K. Quantitative characterization of porosity in laser welds of stainless steel[J]. Scripta Materialia,2012,67(9):783-786.
[30] HOJJATZADEH S M H,PARAB N D,GUO Q,et al. Direct observation of pore formation mechanisms during LPBF additive manufacturing process and high energy density laser welding[J]. International Journal of Machine Tools and Manufacture,2020,153:103555.
[31] KING W E,BARTH H D,CASTILLO V M,et al. Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing[J]. Journal of Materials Processing Technology,2014,214(12):2915-2925.
[32] BAYAT M,THANKI A,MOHANTY S,et al. Keyhole-induced porosities in laser-based powder bed fusion (L-PBF) of Ti6Al4V:High-fidelity modelling and experimental validation[J]. Additive Manufacturing,2019,30:100835.
[33] CUNNINGHAM R,NICOLAS A,MADSEN J,et al. Analyzing the effects of powder and post-processing on porosity and properties of electron beam melted Ti-6Al-4V[J]. Materials Research Letters,2017,5(7):516-525.
[34] CHEN Y,LU F,ZHANG K,et al. Dendritic microstructure and hot cracking of laser additive manufactured Inconel 718 under improved base cooling[J]. Journal of Alloys and Compounds,2016,670:312-321.
[35] CARTER L N,ATTALLAH M M,REED R C,et al. Laser powder bed fabrication of nickel-base superalloys:Influence of parameters; characterisation,quantification and mitigation of cracking[J]. Superalloys,2012,2012(6):2826-2834.
[36] ABOULKHAIR N T,EVERITT N M,ASHCROFT I,et al. Reducing porosity in AlSi10Mg parts processed by selective laser melting[J]. Additive Manufacturing,2014,1:77-86.
[37] PROMOPPATUM P,SRINIVASAN R,QUEK S S,et al. Quantification and prediction of lack-of-fusion porosity in the high porosity regime during laser powder bed fusion of Ti-6Al-4V[J]. Journal of Materials Processing Technology,2022,300:117426.
[38] DARVISH K,CHEN Z W,PASANG T. Reducing lack of fusion during selective laser melting of CoCrMo alloy:Effect of laser power on geometrical features of tracks[J]. Materials & Design,2016,112:357-366.
[39] YIN J,ZHANG W,KE L,et al. Vaporization of alloying elements and explosion behavior during laser powder bed fusion of Cu——10Zn alloy[J]. International Journal of Machine Tools and Manufacture,2021,161:103686.
[40] WEI H L,CAO Y,LIAO W H,et al. Mechanisms on inter-track void formation and phase transformation during laser powder bed fusion of Ti-6Al-4V[J]. Additive Manufacturing,2020,34:101221.
[41] YANG T,LIU T,LIAO W,et al. The influence of process parameters on vertical surface roughness of the AlSi10Mg parts fabricated by selective laser melting[J]. Journal of Materials Processing Technology,2019,266:26-36.
[42] YANG T,LIU T,LIAO W,et al. Laser powder bed fusion of AlSi10Mg:Influence of energy intensities on spatter and porosity evolution,microstructure and mechanical properties[J]. Journal of Alloys and Compounds,2020,849:156300.
[43] LI R,LIU J,SHI Y,et al. Balling behavior of stainless steel and nickel powder during selective laser melting process[J]. The International Journal of Advanced Manufacturing Technology,2012,59:1025-1035.
[44] YAN Q,SONG B,SHI Y. Comparative study of performance comparison of AlSi10Mg alloy prepared by selective laser melting and casting[J]. Journal of Materials Science & Technology,2020,41:199-208.
[45] YANG L,YAN C,CAO W,et al. Compression- compression fatigue behaviour of gyroid-type triply periodic minimal surface porous structures fabricated by selective laser melting[J]. Acta Materialia,2019,181:49-66.
[46] SHIFENG W,SHUAI L,QINGSONG W,et al. Effect of molten pool boundaries on the mechanical properties of selective laser melting parts[J]. Journal of Materials Processing Technology,2014,214(11):2660-2667.
[47] CAI C,WU X,LIU W,et al. Selective laser melting of near-α titanium alloy Ti-6Al-2Zr-1Mo-1V:Parameter optimization,heat treatment and mechanical performance[J]. Journal of Materials Science & Technology,2020,57:51-64.
[48] YE D,FUH J Y H,ZHANG Y,et al. In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks[J]. ISA transactions,2018,81:96-104.
[49] YE D,ZHU K,FUH J Y H,et al. The investigation of plume and spatter signatures on melted states in selective laser melting[J]. Optics & Laser Technology,2019,111:395-406.
[50] ZHANG Y,HONG G S,YE D,et al. Extraction and evaluation of melt pool,plume and spatter information for powder-bed fusion AM process monitoring[J]. Materials & Design,2018,156:458-469.
[51] ZHANG Y,SOON H G,YE D,et al. Powder-bed fusion process monitoring by machine vision with hybrid convolutional neural networks[J]. IEEE Transactions on Industrial Informatics,2019,16(9):5769-5779.
[52] LIN X,WANG Q,FUH J Y H,et al. Motion feature based melt pool monitoring for selective laser melting process[J]. Journal of Materials Processing Technology,2022,303:117523.
[53] ZHANG B,ZIEGERT J,FARAHI F,et al. In situ surface topography of laser powder bed fusion using fringe projection[J]. Additive Manufacturing,2016,12:100-107.
[54] CALTANISSETTA F,GRASSO M,PETRO S,et al. Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion[J]. Additive Manufacturing,2018,24:183-199.
[55] HE P,ZHONG K,LIU X,et al. A phase-guided method for extracting the contour of the fusion area in laser powder bed fusion[C/CD]//International Conference on Optical and Photonic Engineering,2019.
[56] 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,2020,31:375-386.
[57] YANG H,REIJONEN J,REVUELTA A. Multiresolution quality inspection of layerwise builds for metal 3D printer and scanner[J]. Journal of Manufacturing Science and Engineering,2023,145(10):101004.
[58] NGUYEN N V,HUM A J W,DO T,et al. Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion[J]. Virtual and Physical Prototyping,2023,18(1):e2129396.
[59] SCHMITT A,SAUER C,H O FFLIN D,et al. Powder bed monitoring using semantic image segmentation to detect failures during 3D metal printing[J]. Sensors,2023,23(9):4183.
[60] 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.
[61] LIU C,WANG R R,HO I,et al. Toward online layer-wise surface morphology measurement in additive manufacturing using a deep learning-based approach[J]. Journal of Intelligent Manufacturing,2022,34(6):2673-2689.
[62] WESTPHAL E,SEITZ H. A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks[J]. Additive Manufacturing,2021,41:101965.
[63] GAIKWAD A,IMANI F,RAO P,et al. Design rules and in-situ quality monitoring of thin-wall features made using laser powder bed fusion[C/CD]//International Manufacturing Science and Engineering Conference, 2019:V001T01A03.
[64] ZUR J U H J,ACHTERHOLD J,KLESZCZYNSKI S,et al. In situ measurement of part geometries in layer images from laser beam melting processes[J]. Progress in Additive Manufacturing,2019,4:155-165.
[65] WILLIAMS R J,PIGLIONE A,R O NNEBERG T,et al. In situ thermography for laser powder bed fusion:Effects of layer temperature on porosity,microstructure and mechanical properties[J]. Additive Manufacturing,2019,30:100880.
[66] REPOSSINI G,LAGUZZA V,GRASSO M,et al. On the use of spatter signature for in-situ monitoring of laser powder bed fusion[J]. Additive Manufacturing,2017,16:35-48.
[67] CAPRIO L,DEMIR A G O K,PREVITALI B. Observing molten pool surface oscillations during keyhole processing in laser powder bed fusion as a novel method to estimate the penetration depth[J]. Additive Manufacturing,2020,36:101470.
[68] GAIKWAD A,GIERA B,GUSS G M,et al. Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion:A single- track study[J]. Additive Manufacturing,2020,36:101659.
[69] YEUNG H,YANG Z,YAN L. A meltpool prediction based scan strategy for powder bed fusion additive manufacturing[J]. Additive Manufacturing,2020,35:101383.
[70] HOOPER P A. Melt pool temperature and cooling rates in laser powder bed fusion[J]. Additive Manufacturing,2018,22:548-559.
[71] ZHANG W,ZENG Y,WANG J,et al. Multi-scale feature pyramid approach for melt track classification in laser powder bed fusion via coaxial high-speed imaging[J]. Computers in Industry,2023,151:103975.
[72] WANG Q,LIN X,DUAN X,et al. Gaussian process classification of melt pool motion for laser powder bed fusion process monitoring[J]. Mechanical Systems and Signal Processing,2023,198:110440.
[73] PANDIYAN V,MASINELLI G,CLAIRE N,et al. Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance[J]. Additive Manufacturing,2022,58:103007.
[74] 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.
[75] RAJ A,OWEN C,STEGMAN B,et al. Predicting mechanical properties from co-axial melt pool monitoring signals in laser powder bed fusion[J]. Journal of Manufacturing Processes,2023,101:181-194.
[76] MOSHIRI M A,PEDERSEN D B,TOSELLO G,et al. Performance evaluation of in-situ near-infrared melt pool monitoring during laser powder bed fusion[J]. Virtual and Physical Prototyping,2023,18(1):e2205387.
[77] LIU R,YANG H. Multimodal probabilistic modeling of melt pool geometry variations in additive manufacturing[J]. Additive Manufacturing,2023,61:103375.
[78] WOLFF S J,WU H,PARAB N,et al. In-situ high-speed X-ray imaging of piezo-driven directed energy deposition additive manufacturing[J]. Scientific Reports,2019,9(1):962.
[79] OGURA T,WAKAI Y,NAKANO S,et al. Transition mechanism of melt depth in vacuum during laser powder bed fusion using in-situ X-ray and thermal imaging[J]. Progress in Additive Manufacturing,2023,4(1):1-13.
[80] POUDEL A,YASIN M S,YE J,et al. Feature-based volumetric defect classification in metal additive manufacturing[J]. Nature Communications,2022,13(1):6369.
[81] LEUNG C L A,MARUSSI S,ATWOOD R C,et al. In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing[J]. Nature Communications,2018,9(1):1355.
[82] MASSIMI L,CLARK S J,MARUSSI S,et al. Time resolved in-situ multi-contrast X-ray imaging of melting in metals[J]. Scientific Reports,2022,12(1):12136.
[83] GUTKNECHT K,CLOOTS M,SOMMERHUBER R,et al. Mutual comparison of acoustic,pyrometric and thermographic laser powder bed fusion monitoring[J]. Materials & Design,2021,210:110036.
[84] DRISSI-DAOUDI R,MASINELLI G,de FORMANOIR C,et al. Acoustic emission for the prediction of processing regimes in Laser Powder Bed Fusion,and the generation of processing maps[J]. Additive Manufacturing,2023,67:103484.
[85] KOUPRIANOFF D,LUWES N,NEWBY E,et al. On-line monitoring of laser powder bed fusion by acoustic emission:Acoustic emission for inspection of single tracks under different powder layer thickness[C/CD]//2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics,30 November 2017-01 December 2017,Bloemfontein,South Africa.
[86] SHEVCHIK S A,MASINELLI G,KENEL C,et al. Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission[J]. IEEE Transactions on Industrial Informatics,2019,15(9):5194-5203.
[87] WANG H,LI B,XUAN F. Acoustic emission for in situ process monitoring of selective laser melting additive manufacturing based on machine learning and improved variational modal decomposition[J]. The International Journal of Advanced Manufacturing Technology,2022,122(5-6):2277-2292.
[88] ZHIRNOV I,KOUPRIANOFF D. Acoustic diagnostic of laser powder bed fusion processes[C]//Swedish Production Symposium,2022:542-552.
[89] EVERTON S,DICKENS P,TUCK C,et al. Evaluation of laser ultrasonic testing for inspection of metal additive manufacturing[C/CD]//Conference on Laser 3D Manufacturing II,San Francisco,CA,2015.
[90] MILLON C E L,VANHOYE A,OBATON A C C O,et al. Development of laser ultrasonics inspection for online monitoring of additive manufacturing[J]. Welding in the World,2018,62:653-661.
[91] ZHANG J,WU J,ZHAO X,et al. Laser ultrasonic imaging for defect detection on metal additive manufacturing components with rough surfaces[J]. Applied Optics,2020,59(33):10380-10388.
[92] XU W,LI X,ZHANG J. Multi-feature fusion imaging via machine learning for laser ultrasonic based defect detection in selective laser melting part[J]. Optics & Laser Technology,2022,150:107918.
[93] XU W,ZHANG J,LI X,et al. Intelligent denoise laser ultrasonic imaging for inspection of selective laser melting components with rough surface[J]. NDT & E International,2022,125:102548.
[94] CHEN Y,JIANG L,PENG Y,et al. Ultra-fast laser ultrasonic imaging method for online inspection of metal additive manufacturing[J]. Optics and Lasers in Engineering,2023,160:107244.
[95] LIU S,JIA K,WAN H,et al. Inspection of the internal defects with different size in Ni and Ti additive manufactured components using laser ultrasonic technology[J]. Optics & Laser Technology,2022,146:107543.
[96] ZHANG X,SANIIE J,BAKHTIARI S,et al. Unsupervised learning for detection of defects in pulsed infrared thermography of metals[C/CD]//2022 IEEE International Conference on Electro Information Technology,19-21 May 2022,Mankato,MN,USA.
[97] ESTALAKI S M,LOUGH C S,LANDERS R G,et al. Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning[J]. Additive Manufacturing,2022,58:103008.
[98] ZHANG H,VALLABH C K P,ZHAO X. Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF[J]. Additive Manufacturing,2022,58:103075.
[99] LOUGH C S,WANG X,SMITH C C,et al. Correlation of SWIR imaging with LPBF 304L stainless steel part properties[J]. Additive Manufacturing,2020,35:101359.
[100] LIU T,LOUGH C S,SEHHAT H,et al. In-situ infrared thermographic inspection for local powder layer thickness measurement in laser powder bed fusion[J]. Additive Manufacturing,2022,55:102873.
[101] BAUMGARTL H,TOMAS J,BUETTNER R,et al. A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring[J]. Progress in Additive Manufacturing,2020,5(3):277-285.
[102] GRASSO M,LAGUZZA V,SEMERARO Q,et al. In-process monitoring of selective laser melting:spatial detection of defects via image data analysis[J]. Journal of Manufacturing Science and Engineering,2017,139(5):51001.
[103] KHANZADEH M,CHOWDHURY S,MARUFUZZAMAN M,et al. Porosity prediction:Supervised-learning of thermal history for direct laser deposition[J]. Journal of Manufacturing Systems,2018,47:69-82.
[104] MAHMOUDI M,EZZAT A A,ELWANY A. Layerwise anomaly detection in laser powder-bed fusion metal additive manufacturing[J]. Journal of Manufacturing Science and Engineering,2019,141(3):31002.
[105] MITCHELL J A,IVANOFF T A,DAGEL D,et al. Linking pyrometry to porosity in additively manufactured metals[J]. Additive Manufacturing,2020,31:100946.
[106] WILLIAM F T,SHAWN H,SETH S,et al. Finding the limits of single-track deposition experiments:an experimental study of melt pool characterization in laser powder bed fusion[J]. Materials & Design,2023,231:112069.
[107] DENLINGER E R,JAGDALE V,SRINIVASAN G V,et al. Thermal modeling of Inconel 718 processed with powder bed fusion and experimental validation using in situ measurements[J]. Additive Manufacturing,2016,11:7-15.
[108] DUNBAR A J,DENLINGER E R,HEIGEL J,et al. Development of experimental method for in situ distortion and temperature measurements during the laser powder bed fusion additive manufacturing process[J]. Additive Manufacturing,2016,12:25-30.
[109] YAVARI R,WILLIAMS R,RIENSCHE A,et al. Thermal modeling in metal additive manufacturing using graph theory:Application to laser powder bed fusion of a large volume impeller[J]. Additive Manufacturing,2021,41:101956.
[110] REZA YAVARI M,WILLIAMS R J,COLE K D,et al. Thermal modeling in metal additive manufacturing using graph theory:Experimental validation with laser powder bed fusion using in situ infrared thermography data[J]. Journal of Manufacturing Science and Engineering,2020,142(12):121005.
[111] DRYEPONDT S,NANDWANA P,FERNANDEZ- ZELAIA P,et al. Microstructure and high temperature tensile properties of 316L fabricated by laser powder-bed fusion[J]. Additive Manufacturing,2021,37:101723.
[112] EHLERS H,PELKNER M,THEWES R. Online process monitoring for additive manufacturing using eddy current testing with magnetoresistive sensor arrays[J]. IEEE Sensors Journal,2022,22(20):19293-19300.
[113] STOLL P,GASPARIN E,SPIERINGS A,et al. Embedding eddy current sensors into LPBF components for structural health monitoring[J]. Progress in Additive Manufacturing,2021,6(3):445-453.
[114] TODOROV E,BOULWARE P,GAAH K. Demonstration of array eddy current technology for real-time monitoring of laser powder bed fusion additive manufacturing process[C/CD]//SPIE Nondestructive Evaluation And Health Monitoring Conference,2018.
[115] EHLERS H,PELKNER M,THEWES R. Heterodyne eddy current testing using magnetoresistive sensors for additive manufacturing purposes[J]. IEEE Sensors Journal,2020,20(11):5793-5800.
[116] GUO S,REN G,ZHANG B. Subsurface defect evaluation of selective-laser-melted inconel 738LC alloy using eddy current testing for additive/subtractive hybrid manufacturing[J]. Chinese Journal of Mechanical Engineering,2021,34:1-16.
[117] ACCARDI D E,KRANKENHAGEN R,ULBRICHT A,et al. Capability to detect and localize typical defects of laser powder bed fusion (L-PBF) process:An experimental investigation with different non-destructive techniques[J]. Progress in Additive Manufacturing,2022,7(6):1239-1256.
[118] REINARZ B,WITT G. Process monitoring in the laser beam melting process-Reduction of process breakdowns and defective parts[J]. Proc. Mater. Sci. Technol.,2012,2012:9-15.
[119] REN Z,GAO L,CLARK S J,et al. Machine learning-aided real-time detection of keyhole pore generation in laser powder bed fusion[J]. Science,2023,379(6627):89-94.
[120] ZHANG J,ZHAO X,YANG B,et al. Nondestructive evaluation of porosity in additive manufacturing by laser ultrasonic surface wave[J]. Measurement,2022,193:110944.
[121] SHI B,CHEN Z. A layer-wise multi-defect detection system for powder bed monitoring:Lighting strategy for imaging,adaptive segmentation and classification[J]. Materials & Design,2021,210:110035.
[122] CAGGIANO A,ZHANG J,ALFIERI V,et al. Machine learning-based image processing for on-line defect recognition in additive manufacturing[J]. CIRP Annals,2019,68(1):451-454.
[123] MAO Y,LIN H,YU C X,et al. A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures[J]. Journal of Intelligent Manufacturing,2023,34(1):315-329.
[124] KIM J,YANG Z,KO H,et al. Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing[J]. Journal of Manufacturing Systems,2023,68:117-129.
[125] ZHANG B,LIU S,SHIN Y C. In-process monitoring of porosity during laser additive manufacturing process[J]. Additive Manufacturing,2019,28:497-505.
[126] FATHIZADAN S,JU F,LU Y. Deep representation learning for process variation management in laser powder bed fusion[J]. Additive Manufacturing,2021,42:101961.
[127] LARSEN S,HOOPER P A. Deep semi-supervised learning of dynamics for anomaly detection in laser powder bed fusion[J]. Journal of Intelligent Manufacturing,2022,33(2):457-471.
[128] TIAN Q,GUO S,GUO Y,et al. A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition[J]. CIRP Annals,2020,69(1):205-208.
[129] ZHU Q,LIU Z,YAN J. Machine learning for metal additive manufacturing:predicting temperature and melt pool fluid dynamics using physics-informed neural networks[J]. Computational Mechanics,2021,67:619-635.
[130] PAULSON N H,GOULD B,WOLFF S J,et al. Correlations between thermal history and keyhole porosity in laser powder bed fusion[J]. Additive Manufacturing,2020,34:101213.
[131] BARTLETT J L,JARAMA A,JONES J,et al. Prediction of microstructural defects in additive manufacturing from powder bed quality using digital image correlation[J]. Materials Science and Engineering:A,2020,794:140002.
[132] NALAJAM P K,RAMESH V. Microstructural porosity segmentation using machine learning techniques in wire-based direct energy deposition of AA6061[J]. Micron,2021,151:103161.
[133] AMINZADEH M,KURFESS T R. Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images[J]. Journal of Intelligent Manufacturing,2019,30:2505-2523.
[134] SNELL R,TAMMAS-WILLIAMS S,CHECHIK L,et al. Methods for rapid pore classification in metal additive manufacturing[J]. Jom,2020,72:101-109.
[135] YE D S,FUH Y,ZHANG Y J,et al. Defects recognition in selective laser melting with acoustic signals by SVM based on feature reduction[C/CD]//20183rd International Conference on Advanced Materials Research and Manufacturing Technologies.
[136] PARK S,CHOI S,JHANG K. Porosity evaluation of additively manufactured components using deep learning-based ultrasonic nondestructive testing[J]. International Journal of Precision Engineering and Manufacturing-Green Technology,2022,9:395-407.
[137] COECK S,BISHT M,PLAS J,et al. Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data[J]. Additive Manufacturing,2019,25:347-356.
[138] REN K,CHEW Y,ZHANG Y F,et al. Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning[J]. Computer Methods in Applied Mechanics and Engineering,2020,362:112734.
[139] MONTAZERI M,NASSAR A R,DUNBAR A J,et al. In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy[J]. IISE Transactions,2020,52(5):500-515.
[140] CHEN L,YAO X,XU P,et al. Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning[J]. Virtual and Physical Prototyping,2021,16(1):50-67.
[141] BUGATTI M,COLOSIMO B M. Towards real-time in-situ monitoring of hot-spot defects in L-PBF:A new classification-based method for fast video-imaging data analysis[J]. Journal of Intelligent Manufacturing,2022,33(1):293-309.
[142] ZHANG J,WANG P,GAO R X. Deep learning-based tensile strength prediction in fused deposition modeling[J]. Computers in Industry,2019,107:11-21.
[143] AKHAVAN J,LYU J,MANOOCHEHRI S. A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data[J]. Journal of Intelligent Manufacturing,2023,5:1-18.
[144] LI X,ZHANG M,ZHOU M,et al. Qualify assessment for extrusion-based additive manufacturing with 3D scan and machine learning[J]. Journal of Manufacturing Processes,2023,90:274-285.
[145] ZHONG Q,TIAN X,HUANG X,et al. Using feedback control of thermal history to improve quality consistency of parts fabricated via large-scale powder bed fusion[J]. Additive Manufacturing,2021,42:101986.
[146] LI L,ANAND S. Hatch pattern based inherent strain prediction using neural networks for powder bed fusion additive manufacturing[J]. Journal of Manufacturing Processes,2020,56:1344-1352.
[147] LI X,JIA X,YANG Q,et al. Quality analysis in metal additive manufacturing with deep learning[J]. Journal of Intelligent Manufacturing,2020,31:2003-2017.
[148] CHEN R,SODHI M,IMANI M,et al. Brain-inspired computing for in-process melt pool characterization in additive manufacturing[J]. CIRP Journal of Manufacturing Science and Technology,2023,41:380-390.
[149] ESCHNER N,WEISER L,H A FNER B,et al. Classification of specimen density in laser powder bed fusion (L-PBF) using in-process structure-borne acoustic process emissions[J]. Additive Manufacturing,2020,34:101324.
[150] WASMER K,LE-QUANG T,MEYLAN B,et al. In situ quality monitoring in AM using acoustic emission:A reinforcement learning approach[J]. Journal of Materials Engineering and Performance,2019,28:666-672.
[151] MEHTA M,SHAO C. Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing[J]. Journal of Manufacturing Systems,2022,64:197-210.
[152] GAIKWAD A,WILLIAMS R J,de WINTON H,et al. Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing[J]. Materials & Design,2022,221:110919.
[153] SELEZNEV M,GUSTMANN T,FRIEBEL J M,et al. In situ detection of cracks during laser powder bed fusion using acoustic emission monitoring[J]. Additive Manufacturing Letters,2022,3:100099.
[154] ITO K,KUSANO M,DEMURA M,et al. Detection and location of microdefects during selective laser melting by wireless acoustic emission measurement[J]. Additive Manufacturing,2021,40:101915.