Automatic Part Primitive Feature Identification Based on Faceted Models
Feature recognition technology has been developed along with the process of integrating CAD/CAPP/CAM. Automatic feature detection applications based on faceted models expected to speed up the manufacturing process design activities such as setting tool to be used or required machining process in a variety of different features. This research focuses on detection of primitive features available in a part. This is done by applying part slicing and grouping adjacent facets. Type of feature is identified by simply evaluating normal vector direction of all features group. In order to identify features on various planes of a part, planes, one at a time, are rotated to be parallel with the reference plane. The results showed that this method can identify the primitive features automatically accurately in all planes of tested part, this covered : pocket, cylindrical and profile feature.
Keywords: Feature Recognition, Grouping, Normal Vector, Faceted Models.
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ABOUT THE AUTHORS
Gandjar Kiswanto
Gandjar Kiswanto is a lecturer in the Departement of Mechanical Engineering Faculty of Engineering Universitas Indonesia. He earned bachelor degree in Mechanical Engineering Universitas Indonesia in 1995. He completed his master and doctoral education of Production Engineering, Machine Design, Automation fields in the Departement Werktuigkunde - KU. Leuven in 1998 and 2003. In the structure of the department, He served as Secretary of the Department and Head of Design, Manufacturing and Automation Sciences in Department of Mechanical Engineering.
Muizuddin Azka
Muizuddin Azka is a engineer in BPPT (Agency for The Assessment and Application of Technology). He earned bachelor degree in Mechanical Engineering Universitas Sebelas Maret in 2005 and completed his master education in Mechanical Engineering Universitas Indonesia in 2012.
Gandjar Kiswanto
Gandjar Kiswanto is a lecturer in the Departement of Mechanical Engineering Faculty of Engineering Universitas Indonesia. He earned bachelor degree in Mechanical Engineering Universitas Indonesia in 1995. He completed his master and doctoral education of Production Engineering, Machine Design, Automation fields in the Departement Werktuigkunde - KU. Leuven in 1998 and 2003. In the structure of the department, He served as Secretary of the Department and Head of Design, Manufacturing and Automation Sciences in Department of Mechanical Engineering.
Muizuddin Azka
Muizuddin Azka is a engineer in BPPT (Agency for The Assessment and Application of Technology). He earned bachelor degree in Mechanical Engineering Universitas Sebelas Maret in 2005 and completed his master education in Mechanical Engineering Universitas Indonesia in 2012.
