{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T17:37:27Z","timestamp":1769276247776,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom \u2018lightweight\u2019 convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%.<\/jats:p>","DOI":"10.3390\/s23136235","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T01:02:50Z","timestamp":1688950970000},"page":"6235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Damilola","family":"Animashaun","sequence":"first","affiliation":[{"name":"Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8458-6202","authenticated-orcid":false,"given":"Muhammad","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hussain, M., Al-Aqrabi, H., and Hill, R. 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