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Master thesis: Vision and Machine learning to detect small air leaks

Description of the thesis work:
The pneumatic system on a heavy truck is very complex with a high number of pneumatic valves, air lines and fittings. During the assembly process there are several process steps where a small error later can cause a leak in the system.
Today the system is verified by measuring the pressure drop as well as listening for any leaks. It has been confirmed that neither of the two methods will detect very small leaks so there is a strong desire to develop a more capable detection method.
Due to the high variation in product specification and the complexity of the pneumatic system it is not possible to define each possible leak point in advance.
Tasks:
  • Collaborate with our technology supplier to create a process for verification using ultrasonic cameras.
  • Evaluate whether the technology of Ultrasonic cameras together with machine learning is a suitable method of detecting air leaks in the pneumatic system during the assembly process.
  • Proof of concept: Compare the capability of this process versus the existing leak detection processes in the plant.
Education: Computer Science, Software Engineering, Information Technology, Automation and Mechatronics
Education level: Master of Science
Language: Swedish & English
To simplify the collaboration with assembly operators we require that at least one group member speak Swedish.
Number of students: Two (2)
Start date: 2022-01-17
Contact
Mikael Granbom
Process Technology Manager
+46 73 9026329

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