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Smart optical coordinate and surface metrology

Catalucci, Sofia; Thompson, Adam; Eastwood, Joe; Zhang, Zhongyi Michael; Branson, David T; Leach, Richard; Piano, Samanta

Authors

Sofia Catalucci

Joe Eastwood

Zhongyi Michael Zhang

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DAVID BRANSON DAVID.BRANSON@NOTTINGHAM.AC.UK
Professor of Dynamics and Control



Abstract

Manufacturing has recently experienced increased adoption of optimised and fast solutions for checking product quality during fabrication, allowing for manufacturing times and costs to be significantly reduced. Due to the integration of machine learning algorithms, advanced sensors and faster processing systems, smart instruments can autonomously plan measurement pipelines, perform decisional tasks and trigger correctional actions as required. In this paper, we summarise the state of the art in smart optical metrology, covering the latest advances in integrated intelligent solutions in optical coordinate and surface metrology, respectively for the measurement of part geometry and surface texture. Within this field, we include the use of a priori knowledge and implementation of machine learning algorithms for measurement planning optimisation. We also cover the development of multi-sensor and multi-view instrument configurations to speed up the measurement process, as well as the design of novel feedback tools for measurement quality evaluation.

Journal Article Type Article
Acceptance Date Sep 27, 2022
Online Publication Date Sep 27, 2022
Publication Date Oct 19, 2022
Deposit Date Sep 29, 2022
Publicly Available Date Sep 29, 2022
Journal Measurement Science and Technology
Print ISSN 0957-0233
Electronic ISSN 1361-6501
Publisher IOP Publishing
Peer Reviewed Peer Reviewed
Volume 34
Issue 1
Article Number 012001
DOI https://doi.org/10.1088/1361-6501/ac9544
Keywords Applied Mathematics, Instrumentation, Engineering (miscellaneous)
Public URL https://nottingham-repository.worktribe.com/output/11752202
Publisher URL https://iopscience.iop.org/article/10.1088/1361-6501/ac9544

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