Manufacturers strive to produce high-quality products with minimal defect-related costs and rework. Manual quality control processes are often time-consuming and prone to human error.
This project focuses on automating quality control using advanced technologies like machine learning and computer vision, ensuring real-time defect detection, accurate product assessments, and faster feedback loops for manufacturing processes.
Week 1-2 Requirement Gathering and Planning
o Define system requirements o Assessing the manufacturing processes o Identifying the major defect categories
Deliverables: Requirement document, and project roadmap Week 3-4 System Design
Activities o Define system architecture o Select sensors or cameras o Finalize AI model specifications
Deliverables: System design, and architecture document.
Week 5-6: AI Model Development and Training
Activities: Train machine learning models using collected product data.
Deliverables: Trained models and testing results.
Week 7-8: System Integration
Activities: Integrate sensors, cameras, and AI models with the manufacturing line.
Deliverables: Integrated system prototype.
Week 9-10: Testing and Optimization
Activities: Test the system for accuracy, reliability, and real-time performance; optimize for better results.
Deliverables: Test and performance reports.
Week 11-12: Deployment and Documentation
Activities: Deploy the system, train operators, and prepare final documentation.
Deliverables: Deployed system, training materials, and final documentation.
