Quality monitoring based on dynamic resistance and principal component analysis in small scale resistance spot welding process

The present study aims at solving weld quality monitoring problem in small scale resistance spot welding of titanium alloy. Typical dynamic resistance curves were divided into several stages based on the weld nugget formation process. A smaller electrode force or lower welding current was found to promote the initial resistance peak. The bulk material heating stage could not be detected under very high welding current condition. Electrode force effect on dynamic resistance and failure load was much smaller than that of welding current. Principal component analysis was made on discrete dynamic resistance values. The first principal component was selected as independent variable in regression analysis for quality estimation. A back propagation neural network model was then proposed to simultaneously predict the nugget size and failure load. The electrode force, welding current, welding time, and first five principal components were designed as network inputs. Effectiveness of the developed model was validated through data training, testing, and validation. The realtime and online quality monitoring purpose could be realized.