This master thesis focuses on applying deep neural networks (DNNs) in image-based condition monitoring of air-jet spinning machines, specifically focusing on the spinning pressure parameter. The study aims to develop a sensor system to detect structural defects in yarns and assign them to specific machine conditions. The research explores using DNNs to analyze images of yarns generated at different spinning pressures within the spinning box to create a rich dataset for training deep learning models. The study also evaluates the effectiveness of the DNN-based approach in detecting and classifying structural defects in yarns and determining the corresponding machine conditions. The outcomes of this research could potentially help textile enterprises improve the quality and efficiency of their yarn manufacturing processes.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:miun-51239 |
| Date | January 2024 |
| Creators | Jansen, Kai |
| Publisher | Mittuniversitetet, Institutionen för data- och elektroteknik (2023-) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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