Quality Inspection in Casting Aluminum Parts: A Machine Vision System for Filings Detection and Hole Inspection
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| Publicat a: | Journal of Intelligent & Robotic Systems vol. 111, no. 2 (Jun 2025), p. 53 |
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| Altres autors: | , , , , , |
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Springer Nature B.V.
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1007/s10846-025-02251-2 |2 doi | |
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| 045 | 2 | |b d20250601 |b d20250630 | |
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| 100 | 1 | |a Nascimento, Rui |u INESC TEC-Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal (GRID:grid.20384.3d) (ISNI:0000 0001 0756 9687); UTAD - University of Trás-os-Montes and Alto Douro, Vila Real, Portugal (GRID:grid.12341.35) (ISNI:0000 0001 2182 1287) | |
| 245 | 1 | |a Quality Inspection in Casting Aluminum Parts: A Machine Vision System for Filings Detection and Hole Inspection | |
| 260 | |b Springer Nature B.V. |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Quality inspection inspection systems are critical for maintaining product integrity. 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. | |
| 653 | |a Fatigue | ||
| 653 | |a Template matching | ||
| 653 | |a Quality assessment | ||
| 653 | |a Image analysis | ||
| 653 | |a Inspection | ||
| 653 | |a Machine vision | ||
| 653 | |a Image segmentation | ||
| 653 | |a Aluminum | ||
| 653 | |a Instance segmentation | ||
| 653 | |a Computer vision | ||
| 653 | |a Machine learning | ||
| 653 | |a Image processing | ||
| 653 | |a Vision systems | ||
| 653 | |a Filing | ||
| 653 | |a Image processing systems | ||
| 700 | 1 | |a Ferreira, Tony |u INESC TEC-Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal (GRID:grid.20384.3d) (ISNI:0000 0001 0756 9687) | |
| 700 | 1 | |a Rocha, Cláudia D. |u INESC TEC-Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal (GRID:grid.20384.3d) (ISNI:0000 0001 0756 9687) | |
| 700 | 1 | |a Filipe, Vítor |u INESC TEC-Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal (GRID:grid.20384.3d) (ISNI:0000 0001 0756 9687); UTAD - University of Trás-os-Montes and Alto Douro, Vila Real, Portugal (GRID:grid.12341.35) (ISNI:0000 0001 2182 1287) | |
| 700 | 1 | |a Silva, Manuel F. |u INESC TEC-Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal (GRID:grid.20384.3d) (ISNI:0000 0001 0756 9687); ISEP - Polytechnic of Porto, Porto, Portugal (GRID:grid.20384.3d) | |
| 700 | 1 | |a Veiga, Germano |u INESC TEC-Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal (GRID:grid.20384.3d) (ISNI:0000 0001 0756 9687) | |
| 700 | 1 | |a Rocha, Luis |u INESC TEC-Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal (GRID:grid.20384.3d) (ISNI:0000 0001 0756 9687) | |
| 773 | 0 | |t Journal of Intelligent & Robotic Systems |g vol. 111, no. 2 (Jun 2025), p. 53 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3213209910/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3213209910/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |