Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the electrode displacement signal features

To develop an automatic welding quality classification method for the spot welding based on the Chernoff face image created by the electrode displacement signal features, an effective pattern feature extraction method was proposed by which the Chernoff face images were converted to binary ones, and each binary image could be characterized by a binary matrix. According to expression categories on the Chernoff face images, welding quality was classified into five levels and each level just corresponded to a kind of expression. The Hopfield associative memory neural network was used to build a welding quality classifier in which the pattern feature matrices of some weld samples with different welding quality levels were remembered as the stable states. When the pattern feature matrix of a test weld is input into the classifier, it can be converged to the most similar stable state through associative memory, thus, welding quality corresponding to this finally locked stable state can represent the welding quality of the test weld. The classification performance test results show that the proposed method significantly improves the applicability and efficiency of the Chernoff faces technique for spot welding quality evaluation and it is feasible, effective and reliable.