On-line quality estimation of resistance spot welding based on extraction of signals feature

For estimating joint quality of resistance spot welding,joint tensile-shear strength was taken as a kind of evaluation criterion. Welding current,dynamic resistance and electrode displacement signals were simultaneously monitored and collected in welding process.Through character analysis,several characteristic parameters relating to the weld quality were extracted from the three signals.At the same time,based on the correlation analysis results between the parameters and weld strength,7 characteristic parameters were selected to taking as the characteristic pattern of welding process.All characteristic patterns were converted into two-dimension pattern vectors,which were accessed by the computers.Then those patterns were classified according to different welding current.At last,a kind of estimating model of Hopfield neural network was established on the mapping of different pattern vectors and joint strength.Network test results indicated that Hopfield neural network model could get satisfactory effect in resistance spot welding(RSW) quality online evaluation.