Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89022
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | - |
dc.creator | Liu, M | - |
dc.creator | Cheung, CF | - |
dc.creator | Senin, N | - |
dc.creator | Wang, S | - |
dc.creator | Su, R | - |
dc.creator | Leach, R | - |
dc.date.accessioned | 2021-01-15T07:14:54Z | - |
dc.date.available | 2021-01-15T07:14:54Z | - |
dc.identifier.issn | 1084-7529 | - |
dc.identifier.uri | http://hdl.handle.net/10397/89022 | - |
dc.language.iso | en | en_US |
dc.publisher | Optical Society of America | en_US |
dc.rights | Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/). Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. | en_US |
dc.rights | The following publication Mingyu Liu, Chi Fai Cheung, Nicola Senin, Shixiang Wang, Rong Su, and Richard Leach, "On-machine surface defect detection using light scattering and deep learning," J. Opt. Soc. Am. A 37, B53-B59 (2020), is available at https://doi.org/10.1364/JOSAA.394102 | en_US |
dc.title | On-machine surface defect detection using light scattering and deep learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | B53 | - |
dc.identifier.epage | B59 | - |
dc.identifier.volume | 37 | - |
dc.identifier.issue | 9 | - |
dc.identifier.doi | 10.1364/JOSAA.394102 | - |
dcterms.abstract | This paper presents an on-machine surface defect detection system using light scattering and deep learning. A supervised deep learning model is used to mine the information related to defects from light scattering patterns. A convolutional neural network is trained on a large dataset of scattering patterns that are predicted by a rigorous forward scattering model. The model is valid for any surface topography with homogeneous materials and has been verified by comparing with experimental data. Once the neural network is trained, it allows for fast, accurate, and robust defect detection. The system capability is validated on microstructured surfaces produced by ultraprecision diamond machining. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of the Optical Society of America. A, Optics, image science, and vision, 2020, v. 37, no. 9, p. B53-B59 | - |
dcterms.isPartOf | Journal of the Optical Society of America. A, Optics, image science, and vision | - |
dcterms.issued | 2020 | - |
dc.identifier.scopus | 2-s2.0-85090348981 | - |
dc.identifier.pmid | 32902420 | - |
dc.identifier.eissn | 1520-8532 | - |
dc.description.validate | 202101 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Liu_On-machine_surface_defect.pdf | 1.5 MB | Adobe PDF | View/Open |
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