面向激光选区熔化金属增材制造的检测技术研究进展
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摘要
激光选区熔化技术(简称SLM)近些年来发展迅速,已应用于航空航天、医疗、模具等诸多领域。首先综述了近几年SLM领域的在线检测、离线检测技术的进展,重点介绍了SLM的在线检测手段,如利用同轴/旁轴原位架构的高速CCD及红外成像装置获取SLM过程中丰富的可见光和红外信息,介绍了相关描述子提取方法,并研究描述子与SLM成形质量的相关性,另外,少部分学者基于声信号信息源、光电二极管进行了单熔道成形质量的分类识别检测。此外,还介绍了SLM的离线检测手段,除传统的材料测试分析方法外,显微CT和激光诱导击穿光谱学为SLM的缺陷三维表征和成分分析提供了高效新型的工具;在此基础上,进而重点综述了SLM的过程监测及反馈控制策略。其中,介绍了常见的机器学习模型(K均值聚类分析、支持向量机、深度置信网络、卷积神经网络等)及其在SLM过程统计描述子提取中的研究进展。还介绍了统计过程控制方法在SLM的特征量间和特征量与SLM成形质量间的关系分析及控制图生成方面的应用;最后,对SLM在线和离线检测研究进展进行了总结,并对其主要发展方向进行了展望。
Selective laser melting technology(SLM) has developed rapidly in recent years and has been applied in aerospace, medical treatment, mold and many other fields. Firstly, the progress of online detection and offline detection technology in SLM field was reviewed. The online detection methods of SLM were introduced, especially the high-speed CCD and infrared imaging device using coaxial/parallel in-situ architecture to obtain rich visible and infrared information of SLM process. Extraction method of relevant descriptors was introduced and the correlation between descriptors and SLM forming quality was studied. In addition, a small number of scholars have carried out classification and detection of single melt forming quality based on acoustic signal information sources and photodiodes. In addition, the off-line detection method of SLM was introduced. In addition to traditional material testing and analysis methods, micro-CT and laser inductive breakdown spectroscopy provided a new and efficient tool for three-dimensional characterization and composition analysis of SLM defects. On this basis, the process monitoring and feedback control strategies of SLM were summarized. Among them, the common machine learning models(K-means clustering analysis, support vector machine, deep belief network, convolutional neural network, etc.) and their research progress in SLM process statistical descriptor extraction were introduced. The application of statistical process control method in analysis on relationship between the feature quantity of SLM and that between feature quantity and SLM forming quality as well as generation of control chart was introduced. Finally, the research progress of on-line and off-line SLM detection was summarized, and its main development direction was prospected.
Selective laser melting technology(SLM) has developed rapidly in recent years and has been applied in aerospace, medical treatment, mold and many other fields. Firstly, the progress of online detection and offline detection technology in SLM field was reviewed. The online detection methods of SLM were introduced, especially the high-speed CCD and infrared imaging device using coaxial/parallel in-situ architecture to obtain rich visible and infrared information of SLM process. Extraction method of relevant descriptors was introduced and the correlation between descriptors and SLM forming quality was studied. In addition, a small number of scholars have carried out classification and detection of single melt forming quality based on acoustic signal information sources and photodiodes. In addition, the off-line detection method of SLM was introduced. In addition to traditional material testing and analysis methods, micro-CT and laser inductive breakdown spectroscopy provided a new and efficient tool for three-dimensional characterization and composition analysis of SLM defects. On this basis, the process monitoring and feedback control strategies of SLM were summarized. Among them, the common machine learning models(K-means clustering analysis, support vector machine, deep belief network, convolutional neural network, etc.) and their research progress in SLM process statistical descriptor extraction were introduced. The application of statistical process control method in analysis on relationship between the feature quantity of SLM and that between feature quantity and SLM forming quality as well as generation of control chart was introduced. Finally, the research progress of on-line and off-line SLM detection was summarized, and its main development direction was prospected.
引文
[1]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 and Design,2016,95:431-445.
[2]DOUBENSKAIA M,DOMASHENKOV A,SMUROV Iet al.Study of Selective Laser Melting of Intermetallic TiAl Powder Using Integral Analysis[J].International Journal of Machine Tools and Manufacture,2018,129:1-14.
[3]GUSAROV A V,GRIGORIEV S N,VOLOSOVA M A,et al.On Productivity of Laser Additive Manufacturing[J].Journal of Materials Processing Technology,2018,261:213-232.
[4]CHIVEL Y,SMUROV I.On-line Temperature Monitoring in Selective Laser Sintering/Melting[J].Physics Procedia,2010,5:515-521.
[5]CHIVEL Y.Optical In-Process Temperature Monitoring of Selective Laser Melting[J].Physics Procedia,2013,41:904-910.
[6]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.
[7]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,2016,139(5):1-16.
[8]KRAUSS H,ZEUGNER T,ZAEH M F.Layerwise Monitoring of the Selective Laser Melting Process by Thermography[J].Physics Procedia,2014,56:64-71.
[9]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.
[10]LIU Y,YANG Y,MAI S,et al.Investigation into Spatter Behavior During Selective Laser Melting of AISI 316L Stainless Steel Powder[J].Materials&Design,2015,87:797-806.
[11]SAAD E,WANG H,KOVACEVIC R.Classification of Molten Pool Modes in Variable Polarity Plasma Arc Welding Based on Acoustic Signature[J].Journal of Materials Processing Technology,2006,174(1/2/3):127-136.
[12]SONG S,CHEN H,LIN T,et al.Penetration State Recognition Based on the Double-Sound-Sources Characteristic of VPPAW and Hidden Markov Model[J].Journal of Materials Processing Technology,2016,234:33-44.
[13]YE D S,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(5/6/7/8):2791-2801.
[14]YE D S,FUH Y H J,ZHANG Y J,et al.Defects Recognition in Selective Laser Melting with Acoustic Signals by SVM Based on Feature Reduction[J].IOP Conference Series Materials Science and Engineering,2018,436(1):012020.
[15]ZHANG K,LIU T,LIAO W,et al.Photodiode Data Collection and Processing of Molten Pool of Alumina Parts Produced through Selective Laser Melting[J].Optik-International Journal for Light and Electron Optics,2018,156:487-497.
[16]BAEL S V,KERCKHOFS G,MOESEN M,et al.Micro-CT-Based Improvement of Geometrical and Mechanical Controllability of Selective Laser Melted Ti6Al4V Porous Structures[J].Materials Science&Engineering A,2011,528(24):7423-7431.
[17]ZHOU X,WANG D Z,LIU X,et al.3D-Imaging of Selective Laser Melting Defects in a Co-Cr-Mo Alloy by Synchrotron Radiation Micro-CT[J].Acta Materialia,2015,98:1-16.
[18]BOBEL A,HECTOR L G,CHELLADURAI I,et al.In Situ Synchrotron X-ray Imaging of 4140 Steel Laser Powder Bed Fusion[J].Materialia,2019,6:100306.
[19]VRáBEL J,PO?íZKA P,KLUS J,et al.Classification of Materials for Selective Laser Melting by Laser-Induced Breakdown Spectroscopy[J].Chemical Papers,2018:1-9.
[20]THIJS L,VERHAEGHE F,CRAEGHS T,et al.A Study of the Microstructural Evolution during Selective Laser Melting of Ti-6Al-4V[J].Acta Materialia,2010,58(9):3303-3312.
[21]KRUTH J P,FROYEN L,VAN V J,et al.Selective Laser Melting of Iron-Based Powder[J].Journal of Materials Processing Technology,2004,149(1/2/3):616-622.
[22]MERTENS R,VRANCKEN B,HOLMSTOCK N,et al.Influence of Powder Bed Preheating on Microstructure and Mechanical Properties of H13 Tool Steel SLMParts[J].Physics Procedia,2016,83:882-890.
[23]KEMPEN K,THIJS L,VAN H J V,et al.Mechanical Properties of AlSi10Mg Produced by Selective Laser Melting[J].Physics Procedia,2012,39:439-446.
[24]SPEIRS M,WANG X,VAN B S,et al.On the Transformation Behavior of NiTi Shape-Memory Alloy Produced by SLM[J].Shape Memory and Superelasticity,2016,2(4):310-316.
[25]WANG D,YANG Y,LIU R,et al.Study on the Designing Rules and Processability of Porous Structure Based on Selective Laser Melting(SLM)[J].Journal of Materials Processing Technology,2013,213(10):1734-1742.
[26]WANG D,SONG C,YANG Y,et al.Investigation of Crystal Growth Mechanism During Selective Laser Melting and Mechanical Property Characterization of316l Stainless Steel Parts[J].Materials&Design,2016,100:291-299.
[27]JIA Q,GU D.Selective Laser Melting Additive Manufacturing oF Inconel 718 Superalloy Parts:Densification Microstructure AND Properties[J].Journal of Alloys and Compounds,2014,585:713-721.
[28]GU D,HAGEDORN Y C,MEINERS W,et al.Densification Behavior,Microstructure Evolution,and Wear Performance of Selective Laser Melting Processed Commercially Pure Titanium[J].Acta Materialia,2012,60(9):3849-3860.
[29]BAI Y,YANG Y,XIAO Z,et al.Selective Laser Melting of Maraging Steel:Mechanical Properties Development and its Application in Mold[J].Rapid Prototyping Journal,2018,24(3):623-629.
[30]BAI Y,YANG Y,WANG D,et al.Influence Mechanism of Parameters Process and Mechanical Properties Evolution Mechanism of Maraging Steel 300 by Selective Laser Melting[J].Materials Science and Engineering:A,2017,703:116-123.
[31]ZHANG M,YANG Y,WANG D,et al.Effect of Heat Treatment on the Microstructure and Mechanical Properties of Ti6Al4V Gradient Structures Manufactured by Selective Laser Melting[J].Materials Science and Engineering:A,2018,736:288-297.
[32]ZHANG M,YANG Y,SONG C,et al.An Investigation into the Aging Behavior of CoCrMo Alloys Fabricated by Selective Laser Melting[J].Journal of Alloys and Compounds,2018,750:878-886.
[33]YE D S,FUH J Y H,ZHANG Y J,et al.In Situ Monitoring of Selective Laser Melting Using Plume and Spatter Signatures by Deep Belief Networks[J].ISATransactions,2018,81:96-104.
[34]YE D S,FUH Y H J,ZHANG J Y,et al.Defects Recognition in Selective Laser Melting with Acoustic Signals by SVM Based on Feature Reduction[J].IOP Conference Series:Materials Science and Engineering,2018,436(1):012-020.
[35]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.
[36]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.
[37]YUAN B,GIERA B,GUSS G,et al.Semi-Supervised Convolutional Neural Networks for In-Situ Video Monitoring of Selective Laser Melting[C]//2019 IEEE Winter Conference on Applications of Computer Vision(WACV),IEEE,2019.
[38]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.
[39]ZHANG Y J,FUH J Y H,YE D S,et al.In-Situ Monitoring of Laser-Based PBF Via Off-Axis Vision and Image Processing Approaches[J].Additive Manufacturing,2019,25:263-274.
[40]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.
[41]AMINI M,CHANG S I.MLCPM:A Process Monitoring Framework for 3D Metal Printing in Industrial Scale[J].Computers&Industrial Engineering,2018,124:322-330.
[42]MUKHERJEE T,DEBROY T.A Digital Twin for Rapid Qualification of 3D Printed Metallic Components[J].Applied Materials Today,2019,14:59-65.
[43]GARMENDIA I,LEUNDA J,PUJANA J,et al.In-Process Height Control During Laser Metal Deposition Based on Structured Light 3D Scanning[J].Procedia CIRP,2018,68(4):375-380.
[44]GRASSO M,COLOSIMO B M.A Statistical Learning Method for Image-Based Monitoring of the Plume Signature in Laser Powder Bed Fusion[J].Robotics and Computer Integrated Manufacturing,2019,57:103-115.
[45]GRASSO M,DEMIR A G,PREVITALI B,et al.Integrated Manufacturing 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.
[2]DOUBENSKAIA M,DOMASHENKOV A,SMUROV Iet al.Study of Selective Laser Melting of Intermetallic TiAl Powder Using Integral Analysis[J].International Journal of Machine Tools and Manufacture,2018,129:1-14.
[3]GUSAROV A V,GRIGORIEV S N,VOLOSOVA M A,et al.On Productivity of Laser Additive Manufacturing[J].Journal of Materials Processing Technology,2018,261:213-232.
[4]CHIVEL Y,SMUROV I.On-line Temperature Monitoring in Selective Laser Sintering/Melting[J].Physics Procedia,2010,5:515-521.
[5]CHIVEL Y.Optical In-Process Temperature Monitoring of Selective Laser Melting[J].Physics Procedia,2013,41:904-910.
[6]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.
[7]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,2016,139(5):1-16.
[8]KRAUSS H,ZEUGNER T,ZAEH M F.Layerwise Monitoring of the Selective Laser Melting Process by Thermography[J].Physics Procedia,2014,56:64-71.
[9]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.
[10]LIU Y,YANG Y,MAI S,et al.Investigation into Spatter Behavior During Selective Laser Melting of AISI 316L Stainless Steel Powder[J].Materials&Design,2015,87:797-806.
[11]SAAD E,WANG H,KOVACEVIC R.Classification of Molten Pool Modes in Variable Polarity Plasma Arc Welding Based on Acoustic Signature[J].Journal of Materials Processing Technology,2006,174(1/2/3):127-136.
[12]SONG S,CHEN H,LIN T,et al.Penetration State Recognition Based on the Double-Sound-Sources Characteristic of VPPAW and Hidden Markov Model[J].Journal of Materials Processing Technology,2016,234:33-44.
[13]YE D S,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(5/6/7/8):2791-2801.
[14]YE D S,FUH Y H J,ZHANG Y J,et al.Defects Recognition in Selective Laser Melting with Acoustic Signals by SVM Based on Feature Reduction[J].IOP Conference Series Materials Science and Engineering,2018,436(1):012020.
[15]ZHANG K,LIU T,LIAO W,et al.Photodiode Data Collection and Processing of Molten Pool of Alumina Parts Produced through Selective Laser Melting[J].Optik-International Journal for Light and Electron Optics,2018,156:487-497.
[16]BAEL S V,KERCKHOFS G,MOESEN M,et al.Micro-CT-Based Improvement of Geometrical and Mechanical Controllability of Selective Laser Melted Ti6Al4V Porous Structures[J].Materials Science&Engineering A,2011,528(24):7423-7431.
[17]ZHOU X,WANG D Z,LIU X,et al.3D-Imaging of Selective Laser Melting Defects in a Co-Cr-Mo Alloy by Synchrotron Radiation Micro-CT[J].Acta Materialia,2015,98:1-16.
[18]BOBEL A,HECTOR L G,CHELLADURAI I,et al.In Situ Synchrotron X-ray Imaging of 4140 Steel Laser Powder Bed Fusion[J].Materialia,2019,6:100306.
[19]VRáBEL J,PO?íZKA P,KLUS J,et al.Classification of Materials for Selective Laser Melting by Laser-Induced Breakdown Spectroscopy[J].Chemical Papers,2018:1-9.
[20]THIJS L,VERHAEGHE F,CRAEGHS T,et al.A Study of the Microstructural Evolution during Selective Laser Melting of Ti-6Al-4V[J].Acta Materialia,2010,58(9):3303-3312.
[21]KRUTH J P,FROYEN L,VAN V J,et al.Selective Laser Melting of Iron-Based Powder[J].Journal of Materials Processing Technology,2004,149(1/2/3):616-622.
[22]MERTENS R,VRANCKEN B,HOLMSTOCK N,et al.Influence of Powder Bed Preheating on Microstructure and Mechanical Properties of H13 Tool Steel SLMParts[J].Physics Procedia,2016,83:882-890.
[23]KEMPEN K,THIJS L,VAN H J V,et al.Mechanical Properties of AlSi10Mg Produced by Selective Laser Melting[J].Physics Procedia,2012,39:439-446.
[24]SPEIRS M,WANG X,VAN B S,et al.On the Transformation Behavior of NiTi Shape-Memory Alloy Produced by SLM[J].Shape Memory and Superelasticity,2016,2(4):310-316.
[25]WANG D,YANG Y,LIU R,et al.Study on the Designing Rules and Processability of Porous Structure Based on Selective Laser Melting(SLM)[J].Journal of Materials Processing Technology,2013,213(10):1734-1742.
[26]WANG D,SONG C,YANG Y,et al.Investigation of Crystal Growth Mechanism During Selective Laser Melting and Mechanical Property Characterization of316l Stainless Steel Parts[J].Materials&Design,2016,100:291-299.
[27]JIA Q,GU D.Selective Laser Melting Additive Manufacturing oF Inconel 718 Superalloy Parts:Densification Microstructure AND Properties[J].Journal of Alloys and Compounds,2014,585:713-721.
[28]GU D,HAGEDORN Y C,MEINERS W,et al.Densification Behavior,Microstructure Evolution,and Wear Performance of Selective Laser Melting Processed Commercially Pure Titanium[J].Acta Materialia,2012,60(9):3849-3860.
[29]BAI Y,YANG Y,XIAO Z,et al.Selective Laser Melting of Maraging Steel:Mechanical Properties Development and its Application in Mold[J].Rapid Prototyping Journal,2018,24(3):623-629.
[30]BAI Y,YANG Y,WANG D,et al.Influence Mechanism of Parameters Process and Mechanical Properties Evolution Mechanism of Maraging Steel 300 by Selective Laser Melting[J].Materials Science and Engineering:A,2017,703:116-123.
[31]ZHANG M,YANG Y,WANG D,et al.Effect of Heat Treatment on the Microstructure and Mechanical Properties of Ti6Al4V Gradient Structures Manufactured by Selective Laser Melting[J].Materials Science and Engineering:A,2018,736:288-297.
[32]ZHANG M,YANG Y,SONG C,et al.An Investigation into the Aging Behavior of CoCrMo Alloys Fabricated by Selective Laser Melting[J].Journal of Alloys and Compounds,2018,750:878-886.
[33]YE D S,FUH J Y H,ZHANG Y J,et al.In Situ Monitoring of Selective Laser Melting Using Plume and Spatter Signatures by Deep Belief Networks[J].ISATransactions,2018,81:96-104.
[34]YE D S,FUH Y H J,ZHANG J Y,et al.Defects Recognition in Selective Laser Melting with Acoustic Signals by SVM Based on Feature Reduction[J].IOP Conference Series:Materials Science and Engineering,2018,436(1):012-020.
[35]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.
[36]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.
[37]YUAN B,GIERA B,GUSS G,et al.Semi-Supervised Convolutional Neural Networks for In-Situ Video Monitoring of Selective Laser Melting[C]//2019 IEEE Winter Conference on Applications of Computer Vision(WACV),IEEE,2019.
[38]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.
[39]ZHANG Y J,FUH J Y H,YE D S,et al.In-Situ Monitoring of Laser-Based PBF Via Off-Axis Vision and Image Processing Approaches[J].Additive Manufacturing,2019,25:263-274.
[40]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.
[41]AMINI M,CHANG S I.MLCPM:A Process Monitoring Framework for 3D Metal Printing in Industrial Scale[J].Computers&Industrial Engineering,2018,124:322-330.
[42]MUKHERJEE T,DEBROY T.A Digital Twin for Rapid Qualification of 3D Printed Metallic Components[J].Applied Materials Today,2019,14:59-65.
[43]GARMENDIA I,LEUNDA J,PUJANA J,et al.In-Process Height Control During Laser Metal Deposition Based on Structured Light 3D Scanning[J].Procedia CIRP,2018,68(4):375-380.
[44]GRASSO M,COLOSIMO B M.A Statistical Learning Method for Image-Based Monitoring of the Plume Signature in Laser Powder Bed Fusion[J].Robotics and Computer Integrated Manufacturing,2019,57:103-115.
[45]GRASSO M,DEMIR A G,PREVITALI B,et al.Integrated Manufacturing 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.