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Auto-recognition and generation of material removal volume for regular form surface and its volumetric features using volume decomposition method

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Abstract

Computer-aided process planning (CAPP) plays a vital role in manufacturing of a product by computer-aided techniques. Auto-recognition of a surface’s volumetric features and segregation of auto-generated material removal volume into feature volumes is necessary to achieve successful downstream manufacturing activities like automated process planning and scheduling. Only few studies generated material removal volume for a product but resulted in drawbacks such as material removal volume discontinuity between two adjacent material removal volumes generated from two adjacent faces that form convex geometry, and material removal volume overlap between two adjacent material removal volumes generated from two adjacent faces that form concave geometry. An effort has been made to newly classify the regular form surface features and develop an algorithm that automatically recognizes regular form computer-aided design (CAD) model’s surface and their volumetric features, and also auto-generate material removal volume for the recognized features. The surface and volumetric features of a CAD model are successfully recognized by the developed algorithm, and estimated material removal volume of these features is obtained from stock model. The material removal volume discontinuity and overlap limitation that occurred in few studies are eliminated. The percentage difference in overall delta volume (ODV) obtained by algorithm and by manual calculations is less than 0.01 %. Exploded view of auto-generated material removal volumes is obtained for subsequent process planning applications.

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Correspondence to Mohd Salman Abu Mansor.

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Kataraki, P.S., Abu Mansor, M.S. Auto-recognition and generation of material removal volume for regular form surface and its volumetric features using volume decomposition method. Int J Adv Manuf Technol 90, 1479–1506 (2017). https://doi.org/10.1007/s00170-016-9394-6

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  • DOI: https://doi.org/10.1007/s00170-016-9394-6

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