Automated Machine Inspection & Control System
Industrial inspection system integrating PLC control, SECS/GEM communication, and real-time defect detection
Project Summary
Engineered an automated industrial inspection system integrating PLC-based machine control, SECS/GEM communication, and computer vision for real-time defect detection, improving production quality and operational efficiency.
Problem Statement
- Manual inspection processes led to inconsistent defect detection and human error
- Lack of standardized communication between machines and host systems
- Limited real-time visibility into machine status and inspection results
- Difficulty scaling inspection workflows across multiple production lines
System Architecture
[Architecture Diagram Placeholder]
The system integrates PLC-controlled machinery with a computer vision inspection module and a host communication layer via SECS/GEM protocol. PLC manages hardware-level operations such as sensors, actuators, and conveyor control. A vision processing module (Python + OpenCV) performs real-time image capture and defect detection. The system communicates with factory host systems using SECS/GEM over TCP/IP for equipment control, event reporting, and data exchange. Inspection results and logs are stored in a database for traceability. The architecture supports modular deployment and can be extended across multiple machines in a production environment.
Model & Approach
- Developed PLC logic for synchronized machine operations including triggering cameras and handling inspection workflows
- Implemented computer vision algorithms for defect detection (surface anomalies, alignment issues, pattern inconsistencies)
- Integrated SECS/GEM protocol for standardized communication with factory host systems
- Built real-time data exchange pipeline between inspection module and machine controller
- Optimized inspection timing to align with production cycle without introducing bottlenecks
MLOps & Deployment
- Deployed vision modules in a modular architecture allowing updates without stopping machine operations
- Implemented logging and traceability for inspection results and machine events
- Version-controlled inspection algorithms and configurations
- Monitored system performance including detection accuracy and processing latency
- Designed system for future integration with AI/ML-based defect classification models
Results & Impact
- Improved defect detection consistency compared to manual inspection
- Reduced production downtime through automated inspection workflows
- Enabled real-time monitoring and reporting via SECS/GEM integration
- Increased throughput by aligning inspection with machine cycle time
- Enhanced traceability of inspection data for quality audits
Lessons Learned
- Tight synchronization between PLC and vision systems is critical for real-time inspection
- Industrial communication protocols (SECS/GEM) are essential for scalable factory integration
- System reliability and latency are more critical than model complexity in production environments
- Robust error handling is necessary to prevent production line disruptions
- Bridging software and hardware domains requires strong understanding of both timing and control systems