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Auto-recognition and part model complexity quantification of regular-freeform revolved surfaces through delta volume generations

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Abstract

Vast research works implementing feature-based technology have successfully been devoted. However, work on recognition of revolved regular-freeform surfaces is still inadequate due to its complex geometrical properties and topologies resulting lack of its physical significance. This paper presents a new method for recognising both regular and freeform revolved surfaces part model and generates its sub-delta volume using the volume decomposition method. To map the recognised sub-delta volume and respective machining process, part model complexity (PMC) is introduced. Generated sub-delta volumes are classified into three types of revolved surfaces excluding internal features. Sub-delta volumes are generated based on the machining process of roughing and finishing by offsetting the recognised faces. Internal features are de-featured by revolving respective sectioned faces. Differences of the overall delta volume (\(\Delta {\text{ODV}}\)) were calculated and verifications of the proposed PMC were done and presented.

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Acknowledgements

This research is supported by the Ministry of Higher Education Malaysia and Universiti Sains Malaysia under the Fundamental Research Grant Scheme (FRGS) (Reference No. 6071227), Exploratory Research Grant Scheme (ERGS) (Reference No. 6730015), and Research University Grants (Reference Nos. 811186 and 814247). The first author also would like to thank the support of the Universiti Teknologi MARA for the staff’s study sponsorship.

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  1. School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Pulau Pinang, Malaysia

    Ahmad Faiz Zubair & Mohd Salman Abu Mansor

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  1. Ahmad Faiz Zubair
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  2. Mohd Salman Abu Mansor
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Correspondence to Mohd Salman Abu Mansor.

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Zubair, A.F., Abu Mansor, M.S. Auto-recognition and part model complexity quantification of regular-freeform revolved surfaces through delta volume generations. Engineering with Computers 36, 511–526 (2020). https://doi.org/10.1007/s00366-019-00710-7

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