Journal of Advanced Mechanical Design, Systems, and Manufacturing
Online ISSN : 1881-3054
ISSN-L : 1881-3054
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Proposal of a machining feature recognition method to reflect product and manufacturing information
Tetsuya ASANOYuto WATANABEKeiichi NAKAMOTO
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JOURNAL OPEN ACCESS

2022 Volume 16 Issue 1 Pages JAMDSM0015

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Abstract

The recognition of machining features has been considered an important technology in the development of computer-aided process planning (CAPP) systems. Various machining feature recognition methods have been devised that use the geometric information of a target shape and/or the removal volume. However, in the previous studies, geometric dimensioning and tolerancing (GD&T) data are not considered for the recognition. Model-based definition (MBD) is attracting attention as a potential solution to this problem. In MBD, GD&T data as well as notes are referred to as product and manufacturing information (PMI) and directly integrated into a CAD model as 3D annotations. This study aims to propose a machining feature recognition method that reflects PMI for CAPP, based on MBD. In this method, in addition to the previously proposed machining features, dedicated machining features are introduced to deal with a finishing operation. Reference faces to reflect the PMI are then detected from the removal volume, and the PMI is transferred from the target shape to the reference faces. When a finishing operation is required to satisfy PMI, two types of machining features are recognized corresponding to a roughing or finishing operation, respectively. The removal volume obtained from a target shape is also changed to be shifted to practically meet the PMI without modifying the target shape. It also becomes possible to refer to the PMI in operation planning because the recognized machining feature has the PMI. The results of the present case study confirm that PMI can be effectively reflected by the proposed feature recognition method for CAPP.

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© 2022 by The Japan Society of Mechanical Engineers

This article is licensed under a Creative Commons [Attribution-NonCommercial-NoDerivatives 4.0 International] license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
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