Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL)
Perfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impac...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/24/9646 |
| _version_ | 1827636931936124928 |
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| author | Alexander Pierer Markus Hauser Michael Hoffmann Martin Naumann Thomas Wiener Melvin Alexis Lara de León Mattias Mende Jiří Koziorek Martin Dix |
| author_facet | Alexander Pierer Markus Hauser Michael Hoffmann Martin Naumann Thomas Wiener Melvin Alexis Lara de León Mattias Mende Jiří Koziorek Martin Dix |
| author_sort | Alexander Pierer |
| collection | DOAJ |
| description | Perfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts. |
| first_indexed | 2024-03-09T15:52:22Z |
| format | Article |
| id | doaj.art-34760305e4f04e3f9237d014747dc39a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:22Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-34760305e4f04e3f9237d014747dc39a2023-11-24T17:53:02ZengMDPI AGSensors1424-82202022-12-012224964610.3390/s22249646Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL)Alexander Pierer0Markus Hauser1Michael Hoffmann2Martin Naumann3Thomas Wiener4Melvin Alexis Lara de León5Mattias Mende6Jiří Koziorek7Martin Dix8Fraunhofer Institute for Machine Tools and Forming Technology IWU, 09126 Chemnitz, GermanyBenseler Beschichtungen GmbH & Co. KG, 70806 Kornwestheim, GermanyFraunhofer Institute for Machine Tools and Forming Technology IWU, 09126 Chemnitz, GermanyFraunhofer Institute for Machine Tools and Forming Technology IWU, 09126 Chemnitz, GermanyFraunhofer Institute for Machine Tools and Forming Technology IWU, 09126 Chemnitz, GermanyDepartment of Cybernetics and Biomedical Engineering, Technical University of Ostrava (VŠB-TUO), 70833 Ostrava, Czech RepublicFraunhofer Institute for Machine Tools and Forming Technology IWU, 09126 Chemnitz, GermanyDepartment of Cybernetics and Biomedical Engineering, Technical University of Ostrava (VŠB-TUO), 70833 Ostrava, Czech RepublicFraunhofer Institute for Machine Tools and Forming Technology IWU, 09126 Chemnitz, GermanyPerfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts.https://www.mdpi.com/1424-8220/22/24/9646quality controlcoatingextrusionfailureneural networks |
| spellingShingle | Alexander Pierer Markus Hauser Michael Hoffmann Martin Naumann Thomas Wiener Melvin Alexis Lara de León Mattias Mende Jiří Koziorek Martin Dix Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL) Sensors quality control coating extrusion failure neural networks |
| title | Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL) |
| title_full | Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL) |
| title_fullStr | Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL) |
| title_full_unstemmed | Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL) |
| title_short | Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL) |
| title_sort | inline quality monitoring of reverse extruded aluminum parts with cathodic dip paint coating ktl |
| topic | quality control coating extrusion failure neural networks |
| url | https://www.mdpi.com/1424-8220/22/24/9646 |
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