Identification of Freeform Depression Feature in a Part Using Vertex Attributes From Feature Volume / Pramod S Kataraki and Mohd Salman Abu Mansor

• Main Authors: Kataraki, Pramod S (Author), Abu Mansor, Mohd Salman (Author)
• Format: Book
• Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM), 2018.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/39395/1/39395.pdf
• Keywords: TJ Mechanical engineering and machinery

The depression features can be of regular form shape (such as planar, cylindrical) or freeform shape (such as spline) and are found in parts of automotive, ships, aeroplanes like any other protrusion features. Recognition of features by methods like volume decomposition method, hybrid method (face pattern approach and volumetric decomposition method) and attribute based method has been applied in past research works to recognize regular form features of a part. The past research works also express about successful application of volume decomposition method to generate delta volume and recognize regular form volumetric features and application of attribute based method to recognize regular form features like hole, boss present in a cube while the research work on recognition of freeform depression features of a part is found to be inadequate. In this paper an effort is made to develop an algorithm that can recognize freeform depression feature of a part of any form by using vertex attributes of feature volume. First, the algorithm quantifies input part model's volume and identifies the faces having depression feature. Second, the inner loop of each face having depression feature is covered by generating a new face. Third, a lofting operation is performed between new faces to generate feature volume for the depression feature and fourth, the algorithm utilizes vertex attributes of feature volume to recognize feature type. The results obtained in .SAT file format show the algorithm generated feature volume, feature type and results obtained in .TXT file format show the quantitative values of features volume.