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Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts

Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts

T. Srikanth Reddy, M. S. Shunmugam
Copyright: © 2013 |Pages: 28
ISBN13: 9781466618671|ISBN10: 1466618671|EISBN13: 9781466618688
DOI: 10.4018/978-1-4666-1867-1.ch006
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MLA

Reddy, T. Srikanth, and M. S. Shunmugam. "Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts." Dynamic Methods and Process Advancements in Mechanical, Manufacturing, and Materials Engineering, edited by J. Paulo Davim, IGI Global, 2013, pp. 102-129. https://doi.org/10.4018/978-1-4666-1867-1.ch006

APA

Reddy, T. S. & Shunmugam, M. S. (2013). Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts. In J. Davim (Ed.), Dynamic Methods and Process Advancements in Mechanical, Manufacturing, and Materials Engineering (pp. 102-129). IGI Global. https://doi.org/10.4018/978-1-4666-1867-1.ch006

Chicago

Reddy, T. Srikanth, and M. S. Shunmugam. "Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts." In Dynamic Methods and Process Advancements in Mechanical, Manufacturing, and Materials Engineering, edited by J. Paulo Davim, 102-129. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-1867-1.ch006

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Abstract

An automated planning system extracts data from design models and processes it efficiently for transfer to manufacturing activity. Researchers have used face adjacency graphs and volume decomposition approaches which make the feature recognition complex and give rise to multiple interpretations. The present work recognizes the features in prismatic parts considering Attributed Adjacency Matrix (AAM) for the faces of delta volume that lie on rawstock faces. Conceptually, intermediate shape of the workpiece is treated as rawstock for the next stage and tool approach direction is used to recognize minimum, yet practically feasible, set of feature interpretations. Edge-features like fillets/undercuts and rounded/chamfer edges are also recognized using a new concept of Attributed Connectivity Matrix (ACM). In the first module, STEP AP-203 format of a model is taken as the geometric data input. Datum information is extracted from Geometric Dimension and Tolerance (GD&T) data. The second module uses features and datum information to arrive at setup planning and operation sequencing on the basis of different criteria and priority rules.

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