Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki - S Kataraki, Pramodkumar (2019)
The important aspect of computer-aided process planning (CAPP) is to recognize part’s surfaces and features to aid downstream intelligent manufacturing. The automatic recognition of surfaces and features will lead to successful attainment of generative CAPP. Feature recognition works performed so far do not recognize all regular form and freeform volumetric features, and do not generate delta volume (DV) for the recognized features. The works do not address the classification of freeform volumetric features. So there is a need for novel classification of features and approach to auto-recognize features so as to auto-generate DV for each recognized feature for the attainment of generative CAPP. An effort has been made to novel classify the features into regular form and freeform features which are further sub-classified into surface features and volumetric features. The overall delta volume (ODV) is classified into SDVF, SDVT, SDVF filled region, SDV-VF, and SDVR. Algorithm is developed to auto-recognize surfaces of a milling part and auto-generate ODV. The algorithm auto-generates exploded view of ODV, auto-labels the sub-delta volumes (SDVs) and determines the level of complexity to manufacture a part. The generated ODV is validated by percentage error (%) and machining of parts. The algorithm selects the type of machining operation to be performed and auto-allocates each SDV-VF to the face it belongs to. The surface and volumetric features of a part are successfully auto-recognized and estimated DV, results table are auto-generated. The SDVT developed contiguous to SDVF for freeform faces, overcomes the complex DV for roughing process. The DV discontinuity and overlap limitation that occurred in few studies are eliminated. The designation of feature faces and colour coding of faces of SDV-VF expresses the type of feature present in a part. The validation of developed algorithm by percentage error (%) shows error less than 0.1% and the machine selection criteria suggests user the type of milling machine needed to manufacture a part based on level of complexity.