Research progress and prospect of welding intelligent monitoring technology
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摘要:
在国家“十四五”智能制造和 2035 制造业高质量发展远景目标规划下，智能化焊接技术的重要性不言而喻.首先，分析了该技术在学术界和工业界的成果发表情况，对目前成果分布的特点进行了总结. 此外,列出了该学科方向举办的系列重要学术会议，充分展示了该学科的研究热度.其次，分别从声音、光谱、视觉、热学及多信息融合监测角度出发，综述了焊接/增材技术在缺陷在线检测、过程动态表征、质量监控等方面最新的国内外研究进展，表明了多源信息融合技术是焊接智能化监测技术未来发展的主流.最后，总结了现阶段国内焊接智能化-缺陷在线监测基础研究存在的“六多六少”现象，并从多场景拓展应用出发，指出了焊接智能监测技术的未来发展目标与重点突破问题.
Abstract:In the context of the National 14th Five-Year Plan for Intelligent Manufacturing and the Vision 2035 for High-Quality Development of Manufacturing, the significance of intelligent welding technology is readily apparent. First, this paper analyzes the publication status of research results in both academia and industry, summarizing the characteristics of the current distribution of achievements. Additionally, a series of important academic conferences related to this field are listed, highlighting the research popularity of this discipline. Furthermore, this review examines the latest domestic and international research progress in welding and additive manufacturing technologies from the perspectives of acoustics, spectroscopy, vision, thermography, and multi-information fusion monitoring. It indicates that multi-source information fusion technology is likely to be the mainstream approach for the future development of intelligent welding monitoring systems. Finally, the paper concluded the ‘six more and six less’ phenomena existing in the current stage of the domestic welding intelligence - defects online monitoring basic research, and from the multi-scenario to expand the application, pointed out the welding intelligent monitoring technology of the future development goals and key breakthroughs in the problem.
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图 1 基于声发射时频特性的激光焊透监测[5]
Figure 1. Monitoring laser weld penetration using acoustic emission time-frequency characteristics
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图 2 基于电弧声音的TF-CNN熔透识别模型[6]
Figure 2. TF-CNN melt-through detection model based on arc sound signals
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图 4 基于声发射技术的LPBF缺陷监测系统[10]
Figure 4. LPBF defect monitoring system based on acoustic emission technology
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图 8 工业MIG焊熔池图像在线监测系统[22]
Figure 8. Online monitoring system for MIG welding molten pool
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图 9 基于光谱分析的WAAM微观结构研究[26]
Figure 9. Study of WAAM microstructure using spectral analysis
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图 10 基于光谱域知识的WAAM异常状态监测[27]
Figure 10. WAAM anomalous state monitoring based on spectral domain knowledge
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图 11 激光诱导击穿光谱的原位多元素分析[28]
Figure 11. In situ multi-element analysis by laser-induced breakdown spectroscopy
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图 12 层间强制冷却对温度场和应力场的影响[31]
Figure 12. Effect of interlayer forced cooling on the temperature and stress fields
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图 13 基于热红外的电弧增材无损检测框架[32]
Figure 13. A thermal infrared testing framework for arc additive manufacturing
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图 14 多信息融合GMAW焊接缺陷监测系统[43]
Figure 14. Multi-information fusion GMAW welding defect monitoring system
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