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Lett."],"published-print":{"date-parts":[[2026,7,15]]},"abstract":"<jats:p>Localized surface residual stress readily develops in precision optical components during manufacturing and service, degrading optical homogeneity and imaging performance. In this Letter, to address the problems of multiple measurement-condition constraints and stringent adjustment requirements in current measurement techniques during automated detection, we propose a surface residual stress measurement method based on Chromatic Confocal Color (Three-C) decoding. The method exploits the broad axial dispersion range of the chromatic confocal system and the wavelength-color-depth encoding characteristics. By combining photoelasticity with Fresnel reflection theory, a Linearly Polarized Intensity-Based Stress Characterization Model is established through color-model conversion, enabling color-positioning-assisted stress measurement and featuring operational convenience. Experimental results on laser-damaged samples and an externally loaded disk show that the method effectively characterizes the spatial distribution of surface stress fields and reflects stress variation trends under different loading conditions. This approach provides an efficient means for nondestructive evaluation and feedback optimization in the manufacturing of precision optical components.<\/jats:p>","DOI":"10.1364\/ol.604068","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T12:00:22Z","timestamp":1782734422000},"page":"4040","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Chromatic confocal color decoding of surface residual stress in optical components"],"prefix":"10.1364","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5182-0653","authenticated-orcid":true,"given":"Jiayu","family":"Ru","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01t8prc81","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixing","family":"Ye","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01t8prc81","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Bai","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01t8prc81","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ailing","family":"Tian","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01t8prc81","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueliang","family":"Zhu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01t8prc81","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ol-51-14-4040-R1","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1364\/OPTICA.459633","type":"journal-article","volume":"9","author":"Deng","year":"2022","journal-title":"Optica"},{"key":"ol-51-14-4040-R2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1364\/OL.578126","type":"journal-article","volume":"51","author":"Slor","year":"2026","journal-title":"Opt. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}