Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 62, Issue 1, Pages 628-636Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2014.2319216
Keywords
Fault diagnosis; feedforward neural network (FNN); photodiodes; principal component analysis (PCA); process monitoring; support vector machine (SVM); wavelet packet decomposition (WPD)
Categories
Funding
- National Natural Science Foundation of China [51175095]
- Natural Science Foundation of Guangdong Province, China [10251009001000001]
- Guangdong Provincial Project of Science and Technology Innovation of Discipline Construction, China [2013KJCX0063]
Ask authors/readers for more resources
Recent years have seen increasing attention paid to laser welding monitoring. This paper introduces an innovative approach to perform laser welding process monitoring and welded defect diagnosis. The laboratory-scale sensor can be replaced with industrial-scale sensors after the data-driven model has been established by applying multivariate statistics and machine learning methods. In addition, industrial-scale sensor makes effective diagnosis of welded defect by using pattern recognition. Experimental results show that the feature vector affecting estimation and classification accuracy can be obtained by using wavelet packet decomposition principal component analysis. Image processing technology was applied to quantify geometrical parameters of welding process. The feedforward neural network prediction model and the support vector machine classification model built in this research help to guarantee accurate estimation on welding status and effective identification of welded defect. The method proposed by this paper provides an innovative data-driven-based approach for laser welding process monitoring and defects diagnosis.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available