Main Responsibility

个人Google学术 ： https://scholar.google.com/citations?hl=zh-CN&user=TzLg1lEAAAAJ  ，SCI中科院一区论文入选ESI高被引论文，截至2024年7月，论文总引用量1800余次，H因子24。 (Z.Zhang)

 

[1] Z. Zhang, X. Xiang, R. Qin, Z. Du, J. Huang, X. Chen, Y. Su, G. Wen, W. He, X. Chen, Rebalancing Mel-frequency Cepstrum and parallel fusion model for surface hardness monitoring of laser shock peening using acoustic emission, Mechanical Systems and Signal Processing 223 (2025) 111912.

[2] S. Zhang, Z. Zhang, X. Chen, R. Qin, J. Wang, Z. Bai, Z. Li, J. Huang, Y. Su, G. Wen, X. Chen, Intra-layer and inter-layer monitoring of laser powder bed fusion defects based on airborne acoustic and g<SUP>n</SUP>-Res model: pore and deformation, VIRTUAL AND PHYSICAL PROTOTYPING 19(1) (2024).

[3] Y. Yu, Z. Zhang, J. Huang, Y. Li, R. Qin, G. Wen, W. Cheng, X. Chen, Acoustic emission-based weld crack leakage monitoring via FGI and MCCF-CondenseNet convolutional neural network, NDT & E International 148 (2024) 103232.

[4] Z. Li, Z. Zhang, S. Zhang, J. Wang, Z. Bai, Z. Du, K. Huang, Q. Zhang, Y. Su, G. Wen, X. Chen, In-situ monitoring in laser powder bed fusion based on acoustic signal time-frequency synchrosqueezing transform and multi-scale spatially interactive fusion convolutional neural network, Journal of Manufacturing Processes 126 (2024) 471-486.

[5] J. Wang, Z. Zhang, Z. Bai, S. Zhang, R. Qin, J. Huang, G. Wen, On-line defect recognition of MIG lap welding for stainless steel sheet based on weld image and CMT voltage: Feature fusion and attention weights visualization, Journal of Manufacturing Processes 108 (2023) 430-444.

[6] J. Wang, Z. Zhang, R. Qin, G. Wen, Online identification of burn-through and weld deviation in sheet lap MIG welding based on YOLOv5, Measurement Science and Technology 35(2) (2024).

[7] R. Qin, Z. Zhang, J. Huang, Z. Du, X. Xiang, J. Wang, G. Wen, W. He, A novel physically interpretable end-to-end network for stress monitoring in laser shock peening, Computers in Industry 155 (2024).

[8] R. Qin, J. Huang, Z. Zhang, Z. Du, X. Xiang, Y. Yu, G. Wen, W. He, X. Chen, An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals, Mechanical Systems and Signal Processing 208 (2024).

[9] J. Huang, Z. Zhang, R. Qin, Y. Yu, G. Wen, W. Cheng, X. Chen, Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network, Journal of Manufacturing Systems 72 (2024) 93-103.

[10] Z.F. Zhang, Z.M. Liu, R. Qin, G. Li, G.R. Wen, W.F. He, Real-Time Detection of Protective Coating Damage During Laser Shock Peening Based on ReliefF Feature Weight Fusion, Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis 43(8) (2023) 2437-2445.

[11] Z. Zhang, R. Qin, G. Li, Z. Du, G. Wen, W. He, A Novel Approach for Surface Integrity Monitoring in High-Energy Nanosecond-Pulse Laser Shock Peening: Acoustic Emission and Hybrid-Attention CNN, IEEE Transactions on Industrial Informatics 19(3) (2023) 2802-2813.

[12] W. Wang, J. Peng, S. Xie, Z. Zhang, G. Wen, Y. Zhang, H. Wang, Exponential stabilization of aero-engine T-S fuzzy system with decentralized dynamic event-triggered mechanism, Nonlinear Dynamics 111(23) (2023) 21627-21646.

[13] R. Qin, Z. Zhang, G. Li, Z. Du, G. Wen, W. He, Intelligent Monitoring of Surface Hardness Based on Acoustic Emission in Laser Shock Peening, Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 43(4) (2023) 746-752+831.

[14] R. Qin, Z. Zhang, J. Huang, J. Wang, Z. Du, G. Wen, W. He, Surface stress monitoring of laser shock peening using AE time-scale texture image and multi-scale blueprint separable convolutional networks with attention mechanism, Expert Systems with Applications 224 (2023).

[15] R. Qin, Z. Zhang, J. Huang, Z. Du, X. Xiang, G. Wen, W. He, Acoustic emission for surface quality monitoring in laser shock peening via dual-feature fusion convolution neural network, Optics and Laser Technology 164 (2023).

[16] R. Qin, Z. Zhang, Z. Hu, Z. Du, X. Xiang, G. Wen, W. He, On-line evaluation and monitoring technology for material surface integrity in laser shock peening – A review, Journal of Materials Processing Technology 313 (2023).

[17] Z. Li, Z. Zhang, S. Zhang, Z. Bai, R. Qin, J. Huang, J. Wang, K. Huang, Q. Zhang, G. Wen, A novel approach of online monitoring for laser powder bed fusion defects: Air-borne Acoustic Emission and Deep Transfer Learning, Journal of Manufacturing Processes 102 (2023) 579-592.

[18] J. Huang, Z. Zhang, B. Zheng, R. Qin, G. Wen, W. Cheng, X. Chen, Acoustic emission technology-based multifractal and unsupervised clustering on crack damage monitoring for low-carbon steel, Measurement: Journal of the International Measurement Confederation 217 (2023).

[19] J. Huang, Z. Zhang, R. Qin, Y. Yu, G. Wen, W. Cheng, X. Chen, Lightweight Neural Network Architecture for Pipeline Weld Crack Leakage Monitoring Using Acoustic Emission, IEEE Transactions on Instrumentation and Measurement 72 (2023).

[20] J. Huang, Z. Zhang, R. Qin, Y. Yu, Y. Li, G. Wen, W. Cheng, X. Chen, Residual Swin transformer-based weld crack leakage monitoring of pressure pipeline, Welding in the World  (2023).

[21] Z. Zhang, R. Qin, G. Li, Z. Liu, G. Wen, W. He, Online Evaluation of Surface Hardness for Aluminum Alloy in LSP Using Modal Acoustic Emission, IEEE Transactions on Instrumentation and Measurement 71 (2022).

[22] Z. Zhang, R. Qin, G. Li, Z. Du, Z. Li, Y. Lin, W. He, Deep learning-based monitoring of surface residual stress and efficient sensing of AE for laser shock peening, Journal of Materials Processing Technology 303 (2022).

[23] Z. Zhang, Z. Du, R. Qin, G. Li, G. Wen, Surface hardness monitoring of laser shock Peening: Acoustic emission and key frame selection, Measurement: Journal of the International Measurement Confederation 199 (2022).

[24] W. Ren, G. Wen, Z. Zhang, J. Mazumder, Quality monitoring in additive manufacturing using emission spectroscopy and unsupervised deep learning, Materials and Manufacturing Processes 37(11) (2022) 1339-1346.

[25] Z.Y. Du, Z.F. Zhang, R. Qin, G. Li, G.R. Wen, W.F. He, Surface Hardness Monitoring of Laser Shock Peening: Acoustic Emission and Key Frame Selection, Surface Technology 51(11) (2022) 35-44.

[26] H. Zhou, G. Wen, Z. Zhang, X. Huang, S. Dong, Sparse dictionary analysis via structure frequency response spectrum model for weak bearing fault diagnosis, Measurement: Journal of the International Measurement Confederation 174 (2021).

[27] Z. Zhang, R. Qin, Y. Yuan, W. Ren, Z. Yang, G. Wen, Acoustic Emission-Based Weld Crack In-situ Detection and Location Using WT-TDOA, Transactions on Intelligent Welding Manufacturing2021, pp. 49-73.

[28] Z. Zhang, Y. Huang, R. Qin, W. Ren, G. Wen, XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling, Journal of Manufacturing Processes 64 (2021) 30-44.

[29] Z. Zhang, Y. Huang, R. Qin, Z. Lei, G. Wen, Real-Time Measurement of Seam Strength Using Optical Spectroscopy for Al-Li Alloy in Laser Beam Welding, IEEE Transactions on Instrumentation and Measurement 70 (2021).

[30] W. Ren, Z. Zhang, Y. Lu, G. Wen, J. Mazumder, In-Situ Monitoring of Laser Additive Manufacturing for Al7075 Alloy Using Emission Spectroscopy and Plume Imaging, IEEE Access 9 (2021) 61671-61679.

[31] W. Ren, G. Wen, B. Xu, Z. Zhang, A Novel Convolutional Neural Network Based on Time-Frequency Spectrogram of Arc Sound and Its Application on GTAW Penetration Classification, IEEE Transactions on Industrial Informatics 17(2) (2021) 809-819.

[32] Z. Zhang, W. Ren, Z. Yang, G. Wen, Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning, Measurement: Journal of the International Measurement Confederation 156 (2020).

[33] Z. Zhang, L. Zhang, G. Wen, Study of inner porosity detection for Al-Mg alloy in arc welding through on-line optical spectroscopy: Correlation and feature reduction, Journal of Manufacturing Processes 39 (2019) 79-92.

[34] Z. Zhang, Z. Yang, W. Ren, G. Wen, Random forest-based real-time defect detection of Al alloy in robotic arc welding using optical spectrum, Journal of Manufacturing Processes 42 (2019) 51-59.

[35] Z. Zhang, Z. Yang, W. Ren, G. Wen, Condition detection in Al alloy welding process based on deep mining of arc spectrum, Hanjie Xuebao/Transactions of the China Welding Institution 40(1) (2019) 19-25.

[36] Z. Zhang, G. Wen, S. Chen, On-Line Monitoring and Defects Detection of Robotic Arc Welding: A Review and Future Challenges, Transactions on Intelligent Welding Manufacturing2019, pp. 3-28.

[37] Z. Zhang, G. Wen, S. Chen, Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding, Journal of Manufacturing Processes 45 (2019) 208-216.

[38] Y. Zhang, X. Du, G. Wen, X. Huang, Z. Zhang, B. Xu, An adaptive method based on fractional empirical wavelet transform and its application in rotating machinery fault diagnosis, Measurement Science and Technology 30(3) (2019).

[39] R. Zhang, G. Wen, Z. Zhang, B. Xu, Multi-unbalances Identification of Rotor System Integrated with GA-PSO Method, Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 39(4) (2019) 801-809.

[40] Z. Zhang, G. Wen, S. Chen, Audible Sound-Based Intelligent Evaluation for Aluminum Alloy in Robotic Pulsed GTAW: Mechanism, Feature Selection, and Defect Detection, IEEE Transactions on Industrial Informatics 14(7) (2018) 2973-2983.

[41] W. Ren, G. Wen, R. Luan, Z. Yang, Z. Zhang, Single-Channel Blind Source Separation and Its Application on Arc Sound Signal Processing, Transactions on Intelligent Welding Manufacturing2018, pp. 115-126.

[42] W. Ren, G. Wen, S. Liu, Z. Yang, B. Xu, Z. Zhang, Seam Penetration Recognition for GTAW Using Convolutional Neural Network Based on Time-Frequency Image of Arc Sound, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2018, pp. 853-860.

[43] Z. Zhang, S. Chen, Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals, Journal of Intelligent Manufacturing 28(1) (2017) 207-218.

[44] R. Luan, G. Wen, R. Zhang, Z. Chen, Z. Zhang, Porosity defect detection based on FastICA-RBF during pulsed TIG welding process, IEEE International Conference on Automation Science and Engineering, 2017, pp. 548-553.

[45] S. Dong, Z. Zhang, G. Wen, G. Wen, Design and application of unsupervised convolutional neural networks integrated with deep belief networks for mechanical fault diagnosis, 2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings, 2017.

[46] Z. Zhang, G. Wen, S. Chen, Multisensory data fusion technique and its application to welding process monitoring, Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO, 2016, pp. 294-298.

[47] Z. Zhang, G. Wen, An easy method of image feature extraction for real-time welding defects detection, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016, 2016, pp. 615-619.

[48] Z. Zhang, E. Kannatey-Asibu, S. Chen, Y. Huang, Y. Xu, Online defect detection of Al alloy in arc welding based on feature extraction of arc spectroscopy signal, International Journal of Advanced Manufacturing Technology 79(9-12) (2015) 2067-2077.

[49] Z. Zhang, S. Chen, Data-driven feature selection for multisensory quality monitoring in ARC welding, Advances in Intelligent Systems and Computing 363 (2015) 401-410.

[50] Z. Zhang, H. Chen, Y. Xu, J. Zhong, N. Lv, S. Chen, Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding, Mechanical Systems and Signal Processing 60 (2015) 151-165.

[51] P.X. Zhang, Z.F. Zhang, J.H. Chen, Online diagnosis of joints quality in resistance spot welding for sedan body, Advances in Intelligent Systems and Computing 363 (2015) 263-272.

[52] Z.F. Zhang, J.Y. Zhong, Y.X. Chen, S.B. Chen, Feature extraction and modeling of welding quality monitoring in pulsed gas touch argon welding based on arc voltage signal, Journal of Shanghai Jiaotong University (Science) 19(1) (2014) 11-16.

[53] Z. Zhang, X. Chen, H. Chen, J. Zhong, S. Chen, Online welding quality monitoring based on feature extraction of arc voltage signal, International Journal of Advanced Manufacturing Technology 70(9-12) (2014) 1661-1671.

[54] Z. Zhang, H. Yu, N. Lv, S. Chen, Real-time defect detection in pulsed GTAW of Al alloys through on-line spectroscopy, Journal of Materials Processing Technology 213(7) (2013) 1146-1156.

[55] H.W. Yu, Z. Ye, Z.F. Zhang, H.B. Chen, S.B. Chen, Arc spectral characteristics extraction method in pulsed gas tungsten arc welding for Al-Mg Alloy, Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University 47(11) (2013) 1655-1660.

[56] H. Yu, H. Chen, Y. Xu, Z. Zhang, S. Chen, Spectroscopic diagnostics of pulsed gas tungsten arc welding plasma and its effect on weld formation of aluminum-magnesium alloy, Spectroscopy Letters 46(5) (2013) 350-363.

[57] N. Lv, Y. Xu, Z. Zhang, J. Wang, B. Chen, S. Chen, Audio sensing and modeling of arc dynamic characteristic during pulsed Al alloy GTAW process, Sensor Review 33(2) (2013) 141-156.

[58] P. Zhang, Z. Zhang, J. Chen, X. Wang, A diagnosis model for appearance defects of joints in RSW, Hanjie Xuebao/Transactions of the China Welding Institution 32(4) (2011) 5-8.

 

中文论文：

   

  1. 张志芬,  杨哲,  任文静,   温广瑞.    电弧光谱深度挖掘下的铝合金焊接过程状态检测.  焊接学报, 2019,40(1):19-25.                                                                                      EI,   2020年度机械工程学会优秀论文
  2. 张志芬，秦锐，都正尧，李耿，黄婧，温广瑞，激光冲击强化过程监测及其质量在线评估：现状与挑战，航空制造技术，2022.03.25，
  3. 张志芬，张林杰，杨 哲，任文静，温广瑞*，“航空航天用铝合金机器人焊接内部气孔缺陷在线检测”，航空制造技术，2019.62(23-24).14-24                                         中文核心，封面专稿
  4. 张志芬，刘子岷，秦锐，李耿，温广瑞，何卫峰，基于ReliefF特征权重融合的激光冲击强化保护层烧损实时检测，光谱学与光谱分析，2021.06，               2021中国最具国际影响力学术期刊
  5. 李治文，张志芬*，张帅, 陈祯, 张琦, 温广瑞，基于声学监测技术和深度迁移学习的金属激光粉末床熔融缺陷在线监测，2022年全国设备监测诊断与维护学术会议， 2022.08.14， 太原  
  6. 都正尧，张志芬*，秦锐，李耿， 基于声发射与关键帧选择的LSP表面硬度监测，表面技术， 2021.08，                       
  7. 李耿，张志芬* ，秦锐，都正尧， 基于声发射信号的铝合金LSP弹塑性波传播规律探究，表面技术， 2021.08    

专利：

  1. 张志芬,王杰,秦锐,李耿，一种电弧焊接成型缺陷的在线监测方法及装置，2023103088441，实质审查，20230323

  2. 张志芬,王杰,秦锐,李耿，一种薄板搭接电弧焊偏移量在线识别方法、装置和设备，张志芬,王杰,秦锐,李耿，202310425403X，实质审查，20230419

  3. 张志芬，李治文，张琦，都正尧，张帅，王杰，白子健，基于声信号高频信息的激光粉末床熔融缺陷在线监测方法， 2023.4.21，已提交
  4. 张志芬，李治文，张琦，都正尧，张帅，王杰，白子健，基于声信号重要频段的激光粉末床熔融缺陷在线监测方法， 2023.4.21，已提交
  5. 张志芬，于俨龙，黄婧，成玮，陈雪峰，温广瑞，一种基于声发射技术的管道泄漏裂纹形貌在线识别方法,    2023.06， 已提交
  6. 张志芬，秦锐，温广瑞，田增，何卫锋，黄婧，基于内部弹性波非线性特征的激光冲击强化实时监测方法，已公开，2021.7.9，2021102408509  已授权
  7. 张志芬，李耿，秦锐，刘子岷，田增，何卫锋，温广瑞，基于声发射双通道极差的激光冲击强化缺陷在线检测方法，已公开，2021.9.3，CN 113340997 A             实质审查
  8. 张志芬，李耿，秦锐，刘子岷，田增，何卫锋，温广瑞，一种激光冲击强化缺陷实时检测的声发射信号频段选择方法，已公开，2021.9.3，CN 113340995 A           实质审查
  9. 张志芬，李耿，秦锐，刘子岷，田增，何卫锋，温广瑞，基于声发射衰减能量的激光冲击强化缺陷在线检测方法，已公开，2021.9.3，CN 113340996 A                  实质审查
  10. 张志芬，刘子岷，秦锐，李耿，何卫锋，温广瑞，基于模态声发射谱比值的激光冲击强化质量在线监测方法，已公开，2021.9.3，CN 113340493 A                        实质审查
  11. 张志芬，刘子岷，秦锐，李耿，何卫锋，温广瑞，基于时窗能量衰减系数的激光冲击强化质量在线监测方法，已公开，2021.9.14，CN 113390963 A                        实质审查
  12. 张志芬，刘子岷，秦锐，李耿，何卫锋，温广瑞，基于谐波小波频带能量的激光冲击强化质量在线监测方法，已公开，2021.9.3，CN 113340494 A
  13. 张志芬，任文静，栾日维，杨哲，温广瑞，一种铝合金熔焊过程状态检测方法及其装置， 2018.05，已受理
  14. 张志芬, 任文静,杨哲,温广瑞，一种交流钨极氩弧焊电弧声音信号特征快速提取方法， 发明专利，已授权， 2020.03.31， CN 109128446 B
  15. 张志芬, 任文静,杨哲,温广瑞，机器人电弧焊焊接过程控制及多传感信号同步采集系统及方法，已授权，2020.01.17，  CN  108326393  B

软件著作权：

  张志芬, 温广瑞,杨哲, 任文静，机器人焊接质量监控的多信息采集及处理系统软件，2017.11，登记号：2018SR399532