The indentation depth of resistance spot welding joint is closely related to its quality, and the digital image of its
surface is used as an information source. An evaluating algorithm of artificial intelligence for the indentation depth is put
forward. Firstly, through analyzing characteristics of images on the surface of spot welding joints, the first ring area S1,
the second ring area S2, total area S, the area ratio coefficient between total area and first ring area K1, and the area ratio
coefficient between total area and second ring area K2 are extracted as evaluation factors of indentation depth. At the
same time, S2, S, K1 are selected as characteristic parameters of the indentation depth based on the correlation analysis
between the evaluation factors and the indentation depth. Secondly, a support vector machine (SVM) predicting model of
the indentation depth is established. The model selects the parameters S2, S, K1, welding current I, and electrode pressure
F as the input vector and selects the actual indentation depth hT of welding spot as the target vector. Test results are
shown, the correlation coefficient are 0.9958 between model prediction values and actual measured values. The
indentation depth of welding spot can be predicted by means of the SVM evaluating algorithm.
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