This thesis explores and evaluates solutions for the inspection of cutting tool containers at Sandvik Coromant, focusing on the transition from current vision systems utilizing infrared (IR) light to new methods compatible with recycled polypropylene (PP) plastic containers. The primary goal is to evaluate the effectiveness of stereo vision and artificial intelligence (AI) for depth estimation, ensuring that the containers are properly populated with cutting tools. Various methods and algorithms are tested to determine their accuracy and speed, to meet the time requirements of the production line at Sandvik Coromant. The results indicate that, while traditional IR-based systems excel in processing speed and robustness, monocular artificial intelligence methods offer adaptability that could be utilized with the new container material. Future work will involve further optimization and real-world testing to confirm these findings.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-531264 |
| Date | January 2024 |
| Creators | Benkowski, Gustav |
| Publisher | Uppsala universitet, Elektricitetslära |
| 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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