Smart optical coordinate and surface metrology

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

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.

Citation

Catalucci, S., Thompson, A., Eastwood, J., Zhang, Z. M., Branson, D. T., Leach, R., & Piano, S. (2022). Smart optical coordinate and surface metrology. Measurement Science and Technology, 34(1), Article 012001. https://doi.org/10.1088/1361-6501/ac9544

• 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