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文章基本信息

  • 标题:Automatic Part Primitive Feature Identification Based on Faceted Models
  • 本地全文:下载
  • 作者:Gandjar Kiswanto ; Muizuddin Azka
  • 期刊名称:International Journal of Computer Science Issues
  • 印刷版ISSN:1694-0784
  • 电子版ISSN:1694-0814
  • 出版年度:2012
  • 卷号:9
  • 期号:5
  • 出版社:IJCSI Press
  • 摘要: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.
  • 关键词:Feature Recognition; Grouping; Normal Vector; Faceted Models.
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