Abstract:

The metallurgical behavior of aluminum alloy is quite complex in hot working process, especially in resistance spot welding (RSW) with the condition of rapid heating and cooling, thus the crack is one of the major defects of the aluminum alloy RSW. Based on the virtual instrument technology, a system functioning the acquisition and analysis of the acoustic emission signal and the monitoring on the crack of aluminum alloy in RSW process is established on the software LabVIEW platform combining with Matlab. The acoustic emission signal in the cooling crystallization process of aluminum alloy 2A12 RSW, i.e., the hold time of the spot welding cycle, is chose, 4 characteristic parameters associating with the acoustic emission signal intensity, including the ring count, energy, the effective voltage and the energy coefficient from 5-layer wavelet decomposition of 125-250 kHz band, are the input vector, and the crack is the output vector. A three-layer BP neural network monitoring model for the crack of aluminum RSW is established and verified. The verification results shows that the correct rate of the monitoring system reaches 89.1%, thus this study provides an effective method which can monitors the crack of aluminum alloy RSW.

Key words: acoustic emission, crack, neural network, resistance spot welding