Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors
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In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiode sensors, one spectrometer sensor, one ultraviolet and visible light sensing camera and one auxiliary illumination sensing camera, 15 features were analyzed by normalization and principle component analysis, and principle component numbers was chosen as input parameters of support vector machine classification, Three weld quality types were defined according to the weld seam width and weld depth. The overall accuracy of training data was 98%, and the overall accuracy of testing data was 91%, respectively. Experimental results showed that the estimation on welding status was accurate and effective, thus providing an experimental example of monitoring high-power disk laser welding quality.
KeywordsLaser welding Multiple sensors Support vector machine Classification
This work was partly supported by the National Natural Science Foundation of China (51675104), the Science and Technology Planning Public and Construction Project of Guangdong Province, China (Grant No. 2016A010102015), the Research Fund Program of Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing (Grant No. CIMSOF2016008), and the Science and Technology Planning Project of Foshan, China (Grant No. 2014AG10015). Many thanks are given to Katayama Laboratory of Osaka University, for their assistance of laser welding experiments.
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