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周晓晓，徐佳琛，朱大伟，等.弧焊过程质量监测研究现状［J］.电焊机，2023，53（9）：99-107.
ZHOU Xiaoxiao, XU Jiachen, ZHU Dawei, et al.Research Status of Process Quality Monitoring for Arc Welding[J].Electric Welding Machine, 2023, 53(9): 99-107.
随着科技的发展，现代制造业对焊接自动化与智能化的要求越来越高，而焊接过程质量监测是实现焊接自动化与智能化的重要保证。焊接过程质量监测主要包括焊接过程信息采集与分析两方面内容。针对焊接加工方式中应用最广泛的电弧焊方法，从焊接过程监测信息源和数据分析方法两个方面综述了国内外焊接过程质量监测领域的研究现状。前者包括焊接过程工艺信息、电弧声信息、熔池视觉图像信息及多源信息融合等；后者包括统计学分析方法和机器学习方法。最后，提出了现存研究的不足与将来的研究方向。
Welding is an indispensable processing method in modern manufacturing. With the development of modern manufacturing, the demand for welding automation and intelligence is increasing. And welding process quality monitoring is the premise of welding automation and intelligence. Welding process quality monitoring mainly includes two aspects: welding process information collection and analysis. This work describes welding process quality monitor technologies of the most widely used arc welding mainly from the information sensed from the welding and data analytic technologies two aspects. The former includes the instantaneous electrical information, the acoustic information, the optical information and the fusion of multi-source information; and the latter includes statistical methods and machine learning methods. And proposed the shortcomings of existing research and future research needs.
电弧焊质量监测信息传感统计分析机器学习
arc weldingquality monitoringdata sensingstatistical analysismachine learning
Kumar V， Parida M K，Albert S K. The state-of-the-art methodologies for quality analysis of arc welding process using weld data acquisition and analysis techniques［J］. Int. J. Syst. Assur. Eng. Manag，2022， 13， （1）： 34-56.
王昕. 基于CNN和熔池图像的GMAW焊接质量在线监测研究［D］. 黑龙江：哈尔滨工业大学，2019.
WANG X. GMAW Welding Quality Monitoring Based on CNN and Molten Pool Image［D］. Heilongjiang：Harbin Institute of Technology， 2019.
陈华斌，孔萌，吕娜，等.视觉传感技术在机器人智能化焊接中的研究现状［J］.电焊机， 2017， 47（3）：1-16.
CHEN H B， KONG M， LV N， et al. Status and development of vision sensors on intelligentized robotic welding technologies［J］. Electric Welding Machine， 2017， 47（3）：1-16.
彭亚萍. 从数据信息中对焊接的再认识［J］. 电焊机，2022，52（4）：1-7.
PENG Y P. Re-understanding of Welding Process From Data Information［J］. Electric Welding Machine，2022， 52（4）： 1-7.
张广军， 李永哲. 工业4.0语义下智能焊接技术发展综述［J］. 航空制造技术， 2016（11）： 28-33.
ZHANG G J， LI Y Z. Towards intelligent welding in the context of industry 4.0［J］. Aeronaut. Manuf. Technol.， 2016（11）： 28-33.
李江. 智能焊接工业互联网平台［J］. 电焊机， 2021， 51（9）：95-99.
LI J. Intelligent welding industry internet platform［J］. Electric Welding Machine， 2021， 51（9）： 95-99.
林尚扬， 杨学勤， 徐爱杰. 等. 机器人智能化焊接技术发展综述及其在运载火箭贮箱中的应用［J］. 上海航天（中英文）， 2021， 38（3）：8-17.
LIN S Y， YANG X Q，XU A J ， et al. Recent Advances in Intelligentized Robotic Welding Technologies and Its Application in Launch Vehicle Tank［J］. Aerospace Shanghai， 2021， 38（3）：8-17.
陈华斌，陈善本.复杂场景下的焊接智能制造中的信息感知与控制方法［J］.金属学报，2022，58（4）：541-550.
CHEN H B， CHEN S B. Key Information Perception and Control Strategy of Intellignet Welding Under Complex Scene［J］. Acta Metallurgica Sinica， 2022， 58（4）：541-550.
CHEN H， ZHANG B， FUHLBRIGGE T. Welding process optimization methods： a review［M］. Transactions on intelligent welding manufacturing，Singapore： Spri-nger Nature， 2020：3-21.
David S A， Chen J， Gibson B T， et al. Intelligent weld manufacturing： Role of integrated computational welding engineering［C］//Transactions on Intelligent Welding Manufacturing， Singapore： Springer， 2018： 3.
Ogbemhe J， Mpofu K. Towards achieving a fully intelligent robotic arc welding： A review［J］. Ind. Robot： Int. J.， 2015， 42： 475-484.
Jin Z S， Li H C， Jia G Q， et al. Dynamic nonlinear modeling of 3D weld pool surface in GTAW［J］. Robot. Comput. Integr. Manuf.，2016， 39： 1-8.
Severson K， Chaiwatanodom P， Braatz R D. Perspectives on process monitoring of industrial systems［J］. Ann. Rev. Cont.， 2016， 42： 190-200.
Yin S，Ding S X，Xie X C，et al. A review on basic data-driven approaches for industrial process monitoring［J］. IEEE Trans. Ind. Electron.， 2014， 61： 6418-6428.
W Lucas， M Butler. An Evaluation Of Minicomputer Techniques For Data Acquisition And Analysis In Arc Welding Process Research［J］. The Welding Institute hternbers Report， 1981，134：［页码不详］.
Huang Y， Wang K， Fang J， et al. Multifractal analysis for gas metal arc welding［J］. International Journal of Advanced Manufacturing Technology， 2018，94：1903-1910.
周晓晓，王克鸿，杨嘉佳， 等. 等效电阻VMD-奇异谱熵双丝PMIG焊稳定性评价方法［J］. 电焊机， 2018， 48（4）： 18-22.
ZHOU X X， WANG K H， YANG J J， et al. Aluminum alloy Twin-Wire PMIG welding stability evaluation based on equivalent resistance VMD-singular spectral entropy［J］. Electric Welding Machine， 2018， 48（4）： 18-22.
Ferreira G R B， Filho R M M， Scotti A. et al. Exploring a locus of maximum metal transfer stability of the short-circuiting GMAW process based on the reignition voltage peaks［J］. Journal of the Brazilian Society of Mechanical Sciences and Engineering，2021，43：503.
Zhang Z F， Chen X， Chen H， et al. Online welding quality monitoring based on feature extraction of arc voltage signal［J］. Int. J. Adv. Manuf. Technol.， 2014（70）： 1661-1671.
Cui Y X， Shi Y H， Hong X B. Analysis of the frequency features of arc voltage and its application to the recognition of welding penetration in K-TIG welding［J］. Journal of Manufacturing Processes， 2019（46）： 225-233.
吕娜. 基于电弧声信号的铝合金脉冲GTAW熔透特征识别及其实时控制研究［D］. 上海：上海交通大学， 2014.
LV N. Recognition and realtime control of penetration characteristics of Al alloy pulsed GTAW process based on the arc audio information［D］. Shanghai：Shanghai Jiao Tong University， 2014.
ZHANG Z F， WEN G R， CHEN S B， et al. Audible sound-based intelligent evaluation for aluminum alloy in robotic pulsed GTAW：mechanism，feature selection，and defect detection［J］. IEEE Transactions on Industrial Informatics，2018，14： 2973-2983.
Ren W， Wen G， Xu B， et al. A novel Convolutional Neural Network base on Time-frequency Spectrogram of Arc Sound and its Application on GTAW Penetration Classification［J］. IEEE Transactions on Industrial Informatics，2021， 2（17）： 809-819.
González I ， Meruane V， Mendez P F. Deep-learning based analysis of metal-transfer images in GMAW process［J］. Journal of Manufacturing Processes， 2022， 85（7）： 9-20.
Antonello M G， Bracarense A Q， Silva R H G， et al. Electric arc shape and weld bead geometry analysis under the electromagnetic constriction and expansion effect［J］. Int. J. Adv. Manuf. Technol， 2022（118）： 1689-1701.
汤泉， 石志新， 毛志伟. 基于多阈值与神经网络的旋转电弧图像飞溅分析［J］. 焊接学报， 2022， 43（12）：41-46.
TANG Q， SHI Z X， MAO Z W. Spatter analysis of rotating arc image based on multi threshold and neural network［J］. Transactions of the China Welding Institution， 2022， 43（12）：41-46.
Shi Y H， Wang Z S， Chen X Y， et al. Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model［J］. Advances in Manufacturing， 2023， 11： 444-461.
Baek D， Moon H S， Park S H. In-process prediction of weld penetration depth using machine learning-based molten pool extraction technique in tungsten arc welding［J］. Journal of Intelligent Manufacturing，2022：1-17.
Jia C B， Liu X F， Zhang G K， et al. Penetration/keyhole status prediction and model visualization based on deep learning algorithm in plasma arc welding［J］. The International Journal of Advanced Manufacturing Technology， 2021（117）： 3577-3597.
Wang Z， Li L， Chen H， et al. Recognition of GTAW weld penetration based on the lightweight model and transfer learning［J］. Weld World， 2023（67）： 251-264.
Liu L， Chen H， Chen S. Quality analysis of CMT lap welding based on welding electronic parameters and welding sound［J］. Journal of manufacturing processes， 2022， 74：1-13.
刘先淼，岳建锋，黄云龙，等. 窄间隙P-GMAW多信息融合侧壁熔合状态识别研究［J］. 材料科学与工艺， 2023，31（04）： 9-17.
LIU X M， YUE J F， HUANG Y L， et al. Sidewall fusion state recognition of narrow gap P-GMAW based on multi-information fusion［J］. Materials Science and Technology， 2023， 31（04）： 9-17.
Sumesh A， Rameshkumar K， Raja A， et al. Establishing Correlation Between Current and Voltage Signatures of the Arc and Weld Defects in GMAW Process［J］. Arabian journal for Science and Engineering， 2017（42）： 4649-4665.
Kumar V， Albert S K， Chandrasekhar N， et al. Performance Evaluation of Arc Welding Process Using Weld Data Analysis［J］. Transactions of the Indian Institute of Metals， 2018， 71（12）： 3063-3075.
Li C K， Shi Y， Gu Y F， et al. Monitoring weld pool oscillation using reflected laser pattern in gas tungsten arc welding［J］. Journal of Materials Processing Technology， 2018， 255： 876-885.
Benaouda O F， Mezaache M， Bouchakour M， et al. Estimation of the droplet detachment frequency using SSAS and PSD techniques in GMAW process under different transfer modes［J］. The International Journal of Advanced Manufacturing Technology， 2023，126（5-6）： 1979-1996.
Chen Z， Jian C， Feng Z. Welding penetration prediction with passive vision system［J］. Journal of Manufacturing Processes， 2018， 36（12）：224-230.
Feng Y，Chen Z，Wang D，et al. Deep welding：a deep learning enhanced approach to GTAW using multisource sensing images［J］. IEEE Transactions on Industrial Informatics，2019，16（1）： 465-474.
Zhao Z C， Lv N， Xiao R Q， et al. Recognition of penetration states based on arc sound of interest using VGG-SE network during pulsed GTAW process［J］. Journal of Manufacturing Processes， 2023，87： 81-96.
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