Performance Evaluation of the Data Clustering Techniques and Cluster Validity Indices for Efficient Toolpath Development for Incremental Sheet Forming

Abstract

The goal of current research is to compare the data clustering techniques and cluster validity indices for geometrical feature extraction using point cloud. Here, the point clouds are generated by slicing of the computer-aided design (CAD) surface, and the data on each slice is used as inputs to the clustering algorithms. The clustering techniques are used to detect the multiple closed contours on the planer datasets. In this paper, the four most popular clustering techniques, i.e., partition-based (K-means), density-based (DBSCAN), single linkage hierarchical clustering, a variant of hierarchical clustering, and graph-based (spectral) clustering technique are compared using four different datasets. The single linkage hierarchical clustering is preferred over the other variants as it detects the arbitrarily shaped clusters efficiently and effectively. The comparison is based on the ability of successful detection of the closed contours on the planer dataset, the time required, and the input needed for the algorithm. From the investigations, it is found that DBSCAN is the most suitable technique for the feature-based toolpaths (FBTs) development. Besides, for the quality assessment of the clustering solutions and to pinpoint the superlative validity indices, techniques like Calinski-Harabasz, Ball-Hall, Davies-Bouldin, Dunn, Det Ratio, Silhouette, Trace WiB, and Log Det Ratio are compared. The solutions of the clustering techniques are used to compare the validity indices. On the basis of comparative analysis, it is concluded that the Ball-Hall, Dunn, Det Ratio, and Log Det Ratio indices are the best validity criterions for the arbitrary shaped closed contours. The overall outcomes of this research will help in building the algorithms for the feature-based toolpath development strategies for the various manufacturing processes using data science and machine learning techniques. © 2021 American Society of Mechanical Engineers (ASME). All rights reserved.