Development of the geometrical feature extraction tool using DBSCAN clustering for toolpath generation in incremental forming

Incremental forming is an emerging manufacturing technique, which allows the forming of the components without product-specific dies. The process uses Computer Numerical Control (CNC) machine tools to form complicated geometries. A punch, mostly a ball end tool, follows the toolpath obtained from the 3D model of the desired geometry to deform a blank into the desired shape. The objective of the current research is to develop a geometrical feature extraction technology to generate the toolpaths for the incremental forming process. A novel geometrical feature extraction tool, developed using attribute clustering techniques is proposed here. The proposed technology extracts geometrical features from the sliced contoured data of the geometry using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering and convex hull algorithms. Initially, the DBSCAN clustering technique is used for parent feature extraction. Later, child features are extracted from the parent features with the help of a convex hull algorithm. This paper discusses in detail the algorithms developed to extract geometrical features. The performance of the developed algorithms is validated with three different multi-featured geometries representing different types of families like geometries with natural partitions (features are connected with edges), geometries with smoothly connected features, and free form geometries. The results show that the techniques work successfully with different complicated geometries to extract the features. The outcome of this research would help evolve a system for an automatic generation of the feature-based toolpaths for the incremental forming (and similar) processes.