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Being a repetitive task, when performed by operators only, it can be slow and error-prone. This paper introduces an automated inspection system for quality assessment in casting aluminum parts resorting to a robotic system. The method comprises two processes: filing detection and hole inspection. For filing detection, five deep learning modes were trained. These models include an object detector and four instance segmentation models: YOLOv8, YOLOv8n-seg, YOLOv8s-seg, YOLOv8m-seg, and Mask R-CNN, respectively. Among these, YOLOv8s-seg exhibited the best overall performance, achieving a recall rate of 98.10%, critical for minimizing false negatives and yielding the best overall results. Alongside, the system inspects holes, utilizing image processing techniques like template-matching and blob detection, achieving a 97.30% accuracy and a 2.67% Percentage of Wrong Classifications. The system improves inspection precision and efficiency while supporting sustainability and ergonomic standards, reducing material waste and reducing operator fatigue.<\/jats:p>","DOI":"10.1007\/s10846-025-02251-2","type":"journal-article","created":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T04:24:13Z","timestamp":1745641453000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Quality Inspection in Casting Aluminum Parts: A Machine Vision System for Filings Detection and Hole Inspection"],"prefix":"10.1007","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7410-517X","authenticated-orcid":false,"given":"Rui","family":"Nascimento","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6688-406X","authenticated-orcid":false,"given":"Tony","family":"Ferreira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7254-0346","authenticated-orcid":false,"given":"Cl\u00e1udia D.","family":"Rocha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-6577","authenticated-orcid":false,"given":"V\u00edtor","family":"Filipe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0593-2865","authenticated-orcid":false,"given":"Manuel F.","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3044-6938","authenticated-orcid":false,"given":"Germano","family":"Veiga","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8680-4290","authenticated-orcid":false,"given":"Luis","family":"Rocha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"2251_CR1","doi-asserted-by":"publisher","first-page":"107674","DOI":"10.1016\/j.optlaseng.2023.107674","volume":"168","author":"Q Sun","year":"2023","unstructured":"Sun, Q., Xu, K., Liu, H., Wang, J.: Unsupervised surface defect detection of aluminum sheets with combined bright-field and dark-field illumination. 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