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Project # 170
Generative AI–Based Manufacturing Quality Issue Analyzer
| Team Lead: | Ajinkya Sanjay Kadam |
| Other Team Members: | Jaadullah Mohammad |
| Faculty Advisor(s): | Aera LeBoulluec |
| Department: | IMSE |
| Project Type: | Graduate Student |
| Title: | Generative AI–Based Manufacturing Quality Issue Analyzer |
| Abstract: | Manufacturing industries frequently encounter quality defects that require rapid identification and effective corrective actions to maintain productivity and product reliability. This project presents a Generative AI-based Quality Issue Analyzer designed to assist engineers in identifying and addressing manufacturing defects efficiently. The system accepts either structured defect data through a CSV dataset or manual defect descriptions provided by the user. Using a Large Language Model (LLM), the tool analyzes defect attributes such as defect type, location, severity, and inspection method to generate structured engineering recommendations. The output includes the issue category, practical engineering solutions, implementation priority, and expected operational impact. To improve reliability, the system also integrates a rule-based validation layer that ensures priority recommendations align with defect severity levels. This hybrid AI approach combines data processing, prompt engineering, and deterministic validation to support faster decision-making in manufacturing quality control. The proposed tool demonstrates how Generative AI can be used as a practical decision-support system for engineers to improve defect management, process optimization, and product quality. |
| Poster: | View Poster #170 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |