Variable polarity plasma arc welding: Process development and its recent developments of detecting, modeling, and controlling

被引:8
作者
Jiang, Fan [1 ,2 ]
Li, Wenlong [1 ,2 ]
Xu, Bin [1 ,3 ,4 ]
Cheng, Wei [2 ]
Zhang, Guokai [1 ,3 ]
Ma, Xinqiang [2 ]
Chen, Shujun [1 ,4 ]
机构
[1] Beijing Univ Technol, Engn Res Ctr Adv Mfg Technol Automot Components, Minist Educ, Beijing 100124, Peoples R China
[2] Qilu Univ Technol, Laser Inst, Shandong Acad Sci, Jinan 370000, Peoples R China
[3] Harbin Inst Technol, State Key Lab Adv Welding & Joining, Harbin 150001, Peoples R China
[4] Beijing Univ Technol, Phys Sci Res Inst, Fac Sci, Beijing 100124, Peoples R China
基金
中国国家自然科学基金;
关键词
Variable polarity plasma arc welding; Detection; Arc physics; Keyhole; Control; Modeling; ALUMINUM-ALLOY; NUMERICAL-SIMULATION; VPPA-GMAW; RESIDUAL-STRESSES; KEYHOLE PUDDLE; MOLTEN POOL; HEAT INPUT; MICROSTRUCTURE; PENETRATION; OPTIMIZATION;
D O I
10.1016/j.jmapro.2024.01.078
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Variable polarity plasma arc welding (VPPAW) is a highly efficient method for joining aluminum and magnesium alloys, primarily because of the automatic removal of oxide layers through cathode spots. Additionally, the high energy density generated by a variable polarity plasma arc creates a keyhole within the weld pool, enabling single-pass welding of medium-thick plates with minimal deformation and residual stress. Understanding the keyhole behaviors, arc physics, and control methodologies are essential to optimize and improve the stability of this welding process. This survey paper offers an overview of the development of the VPPAW process and its recent developments of detecting, modeling, and controlling. It shows that the potential applications of hybrid processes derived from VPPAW have expanded. Advanced process detection techniques, particularly the utilization of X-ray-based vision systems for the in-situ observation of molten metal flow, have significantly contributed to the understanding of VPPAW. Moreover, studies investigating VPPAW across different welding positions have enhanced the adaptability of this welding method. Accurate numerical models and intelligent deep learning methodologies have been crucial in unveiling the underlying mechanisms and facilitating the predictive analysis of welding quality.
引用
收藏
页码:1 / 17
页数:17
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