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A Machine Vision System for Detecting Automotive Gear Defects

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Title: A Machine Vision System for Detecting Automotive Gear Defects
Author: Allam, Abdelrahman
Department: School of Engineering
Program: Engineering
Advisor: Moussa, Medhat
Abstract: This thesis presents new methods that can be used for the automated quality inspection of automotive gears. Typically, gears are manually inspected at the end of the manufacturing process by trained human inspectors. This process is expensive, somewhat subjective, and suffers from errors due to human fatigue. Automating this process would improve quality and traceability. This would result in fewer rejected gears, thus reducing the overall costs. This thesis investigates multiple approaches ranging from simple image processing techniques to deep learning. The best results were obtained using a hybrid system that combines deep learning with domain knowledge.
URI: https://hdl.handle.net/10214/26378
Date: 2021-09
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Embargoed Until: 2022-08-25


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