Vision–Language AI for Automated Manufacturing Quality Inspection
PhD. Seminar by: Atefeh Gilvari
Date: Nov 20th , 2026
Time: 1 PM – 2:30 PM
Location: (Erie Hall) ER1118
Abstract: Is AI ready to identify a faulty component, pinpoint the problem, and run on a compact device such as a smart camera without human-intervention? This work presents two studies of industrial quality inspection. The first compares AI methods that rely on visual information with vision–language approaches that combine images and texts, examining accuracy, reliability, fault localization, and computational requirements. The second explores how prompts, which can be generated using AI tools such as ChatGPT and Perplexity AI, can guide the identification of faulty components without examples of faulty products or retraining the underlying model. Together, these studies examine AI’s inspection capabilities and the practical challenges of deployment on devices with limited computing resources.
PhD Doctoral Committee:
Internal Reader: Dr. Boubakeur Boufama
Internal Reader: Dr. Arunita Jaekel
External Reader: Dr. Ning Zhang
Advisor(s): Dr. Ziad Kobti , and Dr. Narayan Kar