Title: | Machine Learning based Digital Twin for Monitoring production System of Power Transfer Unit |
Subject: | Computer science, Software engineering |
Level: | Basic, Advanced |
Description: |
Description: Background: The Power Transfer Unit (PTU) is assembled next to the vehicle engine inside the gearbox. From the PTU, a shaft links PTU to the rear-drive unit (RDU). The RDU is located between the rear wheels of vehicles. In the PTU, the Ring gear is welded on a shaft and Pinion (small gear). Ring gear rotates, and this rotation rotates Pinion. The rotation in Pinion rotates the props shaft. During the assembly of a PTU system with inaccurate measurement (e.g., sim dimension) is regarded as scrap. Thus the properties of PTU in the assembly Line need to be observed and monitored where a Digital Twin of such PTU assembly system can assist this fault tolerance process.
Scope: The scope of this thesis project to set up and propose a “Digital Twin” that can be used to monitor an entire chain of the assembly of the Power Transfer Unit (PTU). The Digital Twin should be able to capture properties, measurements, and process steps of the PTUs with the help of Machine Learning and AI.
Goal: Investigate the feasibility of Digital Twin and create one Digital Twin for PTUs in the Assembly Line.
Task Description
· Create a database of Assembly Lines for monitoring.
Reference URL: Lapping is one of the processes of PTU creation, watch the below video for Lapping process https://www.youtube.com/watch?v=UdfdIw6H5Zg
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Start date: | 2023-01-16 |
End date: | 2023-06-30 |
Prerequisites: |
Prerequisite: · Exposure to Software development and programming (MATLAB and Python). · Exposure to embedded systems, Machine Learning or AI-related design activities. · Knowledge of Database design.
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IDT supervisors: | Mobyen Uddin Ahmed |
Examiner: | Shahina Begum |
Comments: | |
Company contact: |
https://www.gknautomotive.com/ |