{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T09:22:40Z","timestamp":1778318560717,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Gansu Provincial Department of Education\u2019s University Teacher Innovation Fund Project","award":["2024B-056"],"award-info":[{"award-number":["2024B-056"]}]},{"name":"Gansu Provincial Department of Education\u2019s University Teacher Innovation Fund Project","award":["62366029"],"award-info":[{"award-number":["62366029"]}]},{"name":"National Natural Science Foundation of China","award":["2024B-056"],"award-info":[{"award-number":["2024B-056"]}]},{"name":"National Natural Science Foundation of China","award":["62366029"],"award-info":[{"award-number":["62366029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.<\/jats:p>","DOI":"10.3390\/s24113621","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T08:43:54Z","timestamp":1717577034000},"page":"3621","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6869-4092","authenticated-orcid":false,"given":"Ruilin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4265-7129","authenticated-orcid":false,"given":"Wenwen","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1007\/s10033-017-0183-4","article-title":"Smart cutting tools and smart machining: Development approaches, and their implementation and application perspectives","volume":"30","author":"Cheng","year":"2017","journal-title":"Chin. 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