Automated Tool Path Generation for End-Milling Operation Using CAD Model in STL Format

A method to extract the machining region from 3D CAD model in STL (Standard Triangulated Language) format and automatically generate tool path is proposed. At first, this study proposes a method to extract the machining region and obtain the geometrical feature such as convex shape or concave shape from only the 3D CAD model in STL format. The STL format has only triangular mesh data and drops all the information which is necessary to extract the removal volume for machining and the geometrical characteristics. Furthermore, the triangular mesh size is non-uniform. Then, the contour line model, in which the product model is minutely divided on the plane along any one axial direction and represented by points at intervals below the indicated resolution obtained from the contour line of the cross section of the product, is proposed. Subsequently, this study proposes a method to determine the machining conditions for each extracted machining region and automatically generate tool path according to the obtained geometrical feature of the machining region.

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.