Industrial Vision-Based Defect Detection

Magna International Inc., headquartered in Ontario, Canada, is North America’s largest auto parts manufacturer. It supplies automotive components and systems to nearly all global automakers, including General Motors, Ford Motor Company, Volkswagen, Daimler, and BMW. With approximately 170,000 employees across 342 manufacturing operations and 91 product development, engineering, and sales centers in 28 countries, Magna has been at the forefront of introducing innovative automotive products to the market for 67 years through its cutting-edge R&D, design, and manufacturing efforts. Magna International Inc. will provide real-world manufacturing data, domain expertise, and deployment environments for vision-based defect detection systems. Magna will offer access to production facilities, historical quality data, and technical mentorship from their R&D and manufacturing teams to guide practical implementation. Magna faces critical quality control challenges including inconsistent manual inspection processes that are prone to human error and cannot scale with high-volume production demands. Currently, Magna employs visionbased defect detection systems to inspect for manufacturing errors, but these systems require optimization to improve accuracy and reliability in identifying defects across diverse product lines. This workplan outlines strategies to enhance these automated inspection capabilities and integrate them more effectively into the production workflow. Current systems struggle with detecting subtle defects across diverse automotive components, leading to costly recalls, warranty claims, and customer dissatisfaction. The company needs automated solutions that maintain quality standards while reducing inspection time and labor costs. Magna will benefit through significant cost reductions from decreased defective products, improved production efficiency, and enhanced customer satisfaction. The automated systems will enable realtime quality monitoring and data-driven process improvements. Society will benefit from safer, higher-quality automotive products, reduced manufacturing waste contributing to environmental sustainability, and advancement of Industry 4.0 technologies that can be adopted across manufacturing sectors. This research establishes a foundation for widespread deployment of AI-driven quality control systems, enhancing global manufacturing competitiveness and product reliability.

Faculty Supervisor:

Beno Benhabib

Student:

Partner:

Magna

Discipline:

Computer science

Sector:

Manufacturing; Wholesale trade

University:

University of Toronto

Program: