Defects such as cracks, overlaps and impressions occur during the production of press-hardened car body components. At present, these types of defects are counteracted in the industrial environment by costly visual inspections carried out by humans. Due to the poor efficiency of visual inspection compared to automated inspection and the risk of defects not being detected, the use of AI-based smart vision sensors is being evaluated in order to enable an automated component inspection process with their help. For the realisation of the test, the most relevant defect types deformation, crack and overlap are identified using a Pareto analysis.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:88342 |
| Date | 28 November 2023 |
| Creators | Simon, Fabio, Werner, Thomas, Weidemann, Andreas, Guilleaume, Christina, Brosius, Alexander |
| Publisher | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 20, urn:nbn:de:bsz:14-qucosa2-882872, qucosa:88287, urn:nbn:de:bsz:14-qucosa2-883431, qucosa:88343 |
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