Li, F. orcid.org/0000-0002-6413-3617, Hiley, J., Syed, T.M. et al. (2 more authors) (2019) A region segmentation method to measure multiple features using a tactile scanning probe. International Journal of Computer Integrated Manufacturing, 32 (6). pp. 569-579. ISSN 0951-192X
Abstract
Coordinate measuring machines (CMMs) have been widely used in industry to precisely measure parts for inspection or quality control. One of the main barriers to using a CMM touch-trigger probe is the cumbersome programming work required to identify the probing points and for scan path planning. In this paper, we propose a practical data-segmentation method to continuously measure multiple features of the workpiece using a scanning probe. This approach takes advantage of the fast data-capture capability of the scanning probe and, subsequently, the point dataset is segmented using the information extracted from the CAD model of the part. This methodology does not require tedious programming and all desired measurement results can be obtained from a single scan. The principle of the method is presented, and the feasibility of the method is experimentally verified on a bridge-type Hexagon DEA Global CMM equipped with a Leitz LSP-X1 probe. The proposed method avoids manual operation errors and generates more sampling points than traditional methods; therefore, theoretically providing lower measurement uncertainty. The test results also indicate that the new method using a scanning probe is easy to implement and can save more than 90% measurement time in comparison with a conventional touch-trigger method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Informa UK Limited, trading as Taylor & Francis Group. This is an author-produced version of a paper subsequently published in International Journal of Computer Integrated Manufacturing. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | CMM; measurement; touch-trigger probe; scanning probe |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > Nuclear Advanced Manufacturing Research Centre |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Apr 2019 13:46 |
Last Modified: | 23 Nov 2021 14:52 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/0951192x.2019.1599431 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145117 |