This project leverages computer vision and AI to enhance quality control processes in manufacturing. The system is trained on datasets of high-quality products and common fault scenarios, such as misaligned components or surface defects. By analyzing high-quality images during production, it detects and flags potential quality faults, reducing waste and improving efficiency in manufacturing.

Features

• Fault Detection: Identifies quality faults such as misaligned components, surface defects, and structural issues.
• High Accuracy: Trained on diverse datasets, ensuring reliable identification of both common and rare defects.
• Real-Time Inspection: Processes high-quality images in real-time to integrate seamlessly into production lines.
• Defect Categorization: Classifies faults to prioritize critical issues for immediate attention.
• Efficiency Improvement: Minimizes manual inspection time and reduces production delays caused by undetected faults.

How It Works

1. Dataset Preparation: Collect images of high-quality products and faulty examples, including scenarios such as misaligned components and surface defects.
2. Model Training: Train AI models using computer vision techniques to differentiate between acceptable and faulty products.
3. Integration: Install high-resolution cameras on manufacturing lines to capture product images.
4. Analysis: AI models analyze captured images in real-time to identify faults.
5. Feedback: Detected faults are flagged, and data is relayed to operators for immediate corrective action.

Challenges Faced

• Creating a diverse and representative dataset for training AI models.
• Ensuring accurate fault detection under varying lighting and environmental conditions.
• Integrating real-time processing into high-speed manufacturing lines.

Future Enhancements

• Expanding detection capabilities to include more complex fault types and variations.
• Incorporating predictive maintenance features to anticipate potential manufacturing issues.
• Developing a feedback loop for continuous improvement of AI model accuracy.
• Integrating with cloud-based systems for centralized fault tracking and analytics.
• Enabling compatibility with various manufacturing setups and product designs.