Data-Driven Real-Time Defect Detection in Polymer Production Lines using Machine Learning
The composite products possess an essential role in different industries, including aerospace and marine. Automation has been introduced in the manufacturing line of composite products aiming to improve the production rate. However, the downside with the automation application in the manufacturing line is the formation of defects in the final product, while the conventional inspection methods (e.g., human eye inspection) are not practical anymore. This project aims to use computers for fast inspection and optimization of the final product.
Intern:
Alireza Ebrahimi
Faculty Supervisor:
Hamidreza Mahyar
Province:
Ontario
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