A novel classification of freeform volumetric features and generative CAPP approach for milling machine selection

Abstract

Feature recognition is an important function of computer-aided process planning (CAPP) system. The freeform feature recognition works performed so far have resulted in successful classification and recognition of freeform surface-based features, but the works do not classify and recognize freeform volumetric features. The research works like automatic generation of delta volume (DV) for volumetric features, finishing, and roughing process were successful when applied to regular form parts, but generated a complex DV for roughing process when applied to freeform parts. Also, the DV generation works do not generate DV for freeform features. So an effort is made (i) to newly classify freeform volumetric features and develop an algorithm to automatically generate DV for freeform volumetric features; (ii) to automatically recognize freeform volumetric features by a set of conditions and colour coding concept; and (iii) to determine the level of complexity and milling machine selection. The problem of complex DV is overcome by generation of sub-delta volume for transition (SDVT). The algorithm is able to recognize freeform volumetric features and the DV’s quantitative data, exploded view, and labelling will aid the downstream activities of CAPP system. The algorithm validation result shows a percentage error of 0.005% for complex part like impeller.