TSI-based hierarchical clustering method and regular-hypersphere model for product quality detection

Abstract

In modern industry, the production process is always accompanied by a large amount of high-dimensional data, which is always too sparse to provide sufficient information for quality detection. Moreover, traditional quality detection methods show the ambiguity and uncertainty in the clustering process. To overcome these difficulties, an improved hierarchical clustering method and a hypersphere model are proposed respectively in this paper. First, the weights of the minimum spanning tree in each cluster are used to calculate the intra-cluster dispersion and the inter-cluster expansion. Next, to enhance the effectiveness of the clustering process, a threshold-based scheduling index is designed to determine whether the merging process for different clusters continues or not, by which the optimal clustering results can be obtained eventually. Then, the obtained clustering results can be used to calculate the centroid of each cluster, thereby to train the judgment boundary to form a hypersphere. Finally, the hypersphere can be used to detect the product quality. The simulation and experimental results show that, the clustering accuracy is close to 100%, and the accuracy of detection model is superior to 91.83%. Those results are competitive with existing state-of-the-art detection methods in terms of clustering effectiveness and the real-time performance.