Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19979
Title: A novel and smart interactive feature recognition system for rotational parts using a STEP file
Authors: Al-wswasi, M
Ivanov, A
Keywords: Smart automatic feature recognition;Computer-aided process planning;STEP AP203;Rotational parts
Issue Date: 30-May-2019
Publisher: Springer Verlag
Citation: International Journal of Advanced Manufacturing Technology, 2019, 104 (1-4), pp. 261 - 284
Abstract: Sharing product design information with other downstream applications, such as process planning, is a major barrier to develop an integrated manufacturing system. Part of this shortcoming is due to the difference in product data descriptions, since a design is geometry based, whereas process planning is manufacturing feature based. The implementation of automatic feature recognition (AFR) techniques is considered an indispensable concept for transferring product data between computer-aided design (CAD) and automatic computer-aided process planning (ACAPP). This is accomplished using one of the international Product Data Exchange (PDE) standards, such as DXF, IGES, or STEP files. Despite different AFR techniques and systems having been developed to serve this aim, each of them has limitations. The most important limitation is that each system is restricted to a specific set of predefined manufacturing features. This means that even when the system tries to cover as many as possible of the existing features that are predefined, it is always possible to create a new feature based on the specific requirements and designer creativity. Consequently, the new feature is not included in the compiled database and, hence, will not be recognised. This paper presents a novel and smart interactive AFR (SI-AFR) methodology for recognising the features of rotational parts, taking a STEP AP203, AP214, or AP242 file as an input to the system. This has been written using C# coding to extract the features’ geometrical and topological information from a STEP file, building a database containing a set of 54 predefined features, whilst also smartly learning how to recognise new features and adding them to the set. Several examples have been processed for this paper to validate the system, and based on the results, it is anticipated that it will lead to the launching of a new generation of feature recognition systems.
URI: http://bura.brunel.ac.uk/handle/2438/19979
DOI: http://dx.doi.org/10.1007/s00170-019-03849-1
ISSN: 0268-3768
Appears in Collections:Dept of Mechanical Aerospace and Civil Engineering Research Papers

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