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Engineering with Computers

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07.02.2019 | Original Article

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

verfasst von: Ahmad Faiz Zubair, Mohd Salman Abu Mansor

Erschienen in: Engineering with Computers | Ausgabe 2/2020

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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.

Vorheriger Artikel Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques

Nächster Artikel Creative design through knowledge clustering and case-based reasoning

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Titel: Auto-recognition and part model complexity quantification of regular-freeform revolved surfaces through delta volume generations
verfasst von: Ahmad Faiz Zubair
Mohd Salman Abu Mansor
Publikationsdatum: 07.02.2019
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 2/2020
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-019-00710-7

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