Part classification method based on developable clustering

TL;DR: In this article, a part classification method based on expansible cluster is proposed, which has clear relevancy analysis, high classification efficiency and high accuracy, but it is not suitable for large numbers of parts.

Abstract: The invention relates to a parts classification method based on expansible cluster, which comprises the following steps: 1) the node domain of the structural characteristic of parts is extracted; 2) the characteristic variable numerical value v and the interval V of the parts are determined; 3) the distance formula of each part structure is established; 4) the structural relevancy T(i, j) of parts Ri and Rj is determined; 5) numerical analysis is carried out to a symmetric matrix M; 6) the average relevancy numerical value of cluster analysis is established; 7) the n minus 2 symmetric matrix is obtained by utilizing the algorithm process of step 5) to the newly formed n minus 1 symmetric matrix, the processes are circulated, and when the minimum relevancy is more than K value, the cluster process is terminated and a unique cluster result is obtained: the category of products and the parts of each category. The invention provides a part classification method based on the expansible cluster, which has clear relevancy analysis, high classification efficiency and high accuracy.