InsightQC is an AI-powered visual inspection and quality control system designed to detect surface defects in 3D-printed parts. By leveraging state-of-the-art object detection (YOLOv8) and Robotic Process Automation (RPA) tools like UiPath, InsightQC automates the end-to-end defect tracking and maintenance reporting process, significantly improving production efficiency in additive manufacturing.
- 🧠 YOLOv8 for accurate real-time defect detection
- 📊 Automated Excel Reports to visualize defect frequency
- 🤖 RPA Integration with UiPath for dynamic maintenance scheduling
- 📂 Roboflow integration for dataset management and training
- 💡 Boosts quality assurance and reduces production downtime
- Input: Capture or upload 3D-printed product images
- Detection: YOLOv8 detects and classifies visible surface defects
- Data Logging: Defect data is stored in an Excel file using Pandas
- Visualization: Charts are generated to analyze defect trends
- Automation: UiPath bot reads data and schedules maintenance based on frequency
| Layer | Tools/Technologies |
|---|---|
| Object Detection | YOLOv8 (Ultralytics), Roboflow |
| Data Processing | Python, Pandas, NumPy |
| Visualization | Matplotlib |
| Automation | UiPath |
| IDEs/Tools | VS Code |