Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 93, Issue -, Pages 634-644Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2017.01.028
Keywords
Small scale resistance spot welding; Titanium alloy; Quality monitoring; Electrode voltage; Dynamic resistance; Neural network
Categories
Funding
- National Natural Science Foundation of China [11072083, 51322507, 51635007]
- Doctoral Dissertation Innovation Fund of Huazhong University of Science and Technology [0118240059]
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Our study aims at developing an effective quality monitoring system in small scale resistance spot welding of titanium alloy. The measured electrical signals were interpreted in combination with the nugget development. Features were extracted from the dynamic resistance and electrode voltage curve. A higher welding current generally indicated a lower overall dynamic resistance level. A larger electrode voltage peak and higher change rate of electrode voltage could be detected under a smaller electrode force or higher welding current condition. Variation of the extracted features and weld quality was found more sensitive to the change of welding current than electrode force. Different neural network model were proposed for weld quality prediction. The back propagation neural network was more proper in failure load estimation. The probabilistic neural network model was more appropriate to be applied in quality level classification. A real-time and on-line weld quality monitoring system may be developed by taldng advantages of both methods. (C) 2017 Elsevier Ltd. All rights reserved.
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