Journal
JOURNAL OF MANUFACTURING PROCESSES
Volume 42, Issue -, Pages 51-59Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jmapro.2019.04.023
Keywords
Random forest-based; Defect detection; Robotic arc welding; Optical spectrum
Categories
Funding
- National Natural Science Foundation of China [51775409, 51605372]
- China postdoctoral science foundation [2018T111052, 2016M602805]
- Program for New Century Excellent Talents in University [NCET-13-0461]
Ask authors/readers for more resources
Aluminum alloy of arc welding is the main technology for the key components manufacturing in aerospace, nuclear power, ship and so on. Real-time weld defects detection is still challenging due to the complexity and diversity of weld defects. Arc optical Spectroscopy emission is the key information generated during arc welding process. However, how to select the effective spectrum feature from high dimension of arc spectrum is crucial for improving the accuracy of defects recognition. This paper proposed an on-line defects detection method for aluminum alloy in robotic arc welding based on random forest and arc spectrum. Firstly, preprocessing of arc spectrum was carried out before 50 features were extracted. Then, a quantitative index of feature importance is proposed based on mean decrease accuracy and mean decrease Gini to reduce the feature redundancy. Six spectral features were selected and analyzed in terms of the construction pattern. Furthermore, the defect identification model was established based on random forest and the optimal feature subset. Comparing with RBF and BP models, it can achieve better performance in identifying three typical defects, including incomplete penetration, bum-through and porosity. This paper can provide some guidance for data mining of optical information and intelligent manufacturing.
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