VCANUS EdgeAI Solutions for Smart Manufacturing
VCANUS delivers cutting-edge EdgeAI solutions, powered by TSLoom and advanced data analytics, to enable manufacturers to achieve real-time process intelligence, predictive maintenance, and data-driven decision-making. Our solutions combine machine learning, time-series analysis, and edge computing to optimize production processes, reduce downtime, and enhance quality—while ensuring scalability, low latency, and seamless integration with existing systems.
Why VCANUS EdgeAI?
VCANUS empowers manufacturers to:
- Predict process outcomes with high accuracy using time-series data.
- Classify and cluster production patterns for deeper insights and efficiency.
- Detect anomalies in real time to prevent defects and minimize waste.
- Deploy lightweight, high-performance AI models directly on edge devices for low-latency decision-making.
- Integrate seamlessly with industrial IoT (IIoT) and MES/ERP systems.
Unlike traditional analytics tools, our solutions are tailored for industrial environments, delivering actionable insights at the edge where data is generated.
Key Solutions
1. Process Prediction
- Forecast critical process parameters (e.g., temperature, pressure, cycle time) using TSLoom-based time-series models.
- Optimize production schedules by predicting bottlenecks and resource demands.
- Improve yield and reduce scrap with data-driven recommendations.
Use Cases:
- Predictive quality control in semiconductor, automotive, and metal fabrication.
- Energy consumption optimization in manufacturing lines.
2. Classification & Clustering
- Classify production states (e.g., normal vs. abnormal operation) for automated quality control.
- Cluster similar process patterns to identify hidden inefficiencies or optimal conditions.
- Enable root-cause analysis by grouping similar anomalies or defects.
Use Cases:
- Defect classification in PCB assembly, casting, and additive manufacturing.
- Process optimization in chemical and pharmaceutical production.
3. Anomaly Detection
- Real-time anomaly detection using unsupervised and supervised learning models.
- Early warning system for equipment failures, reducing unplanned downtime.
- Adaptive thresholds that learn from historical data to minimize false alarms.
Use Cases:
- Fault detection in rotating machinery, pumps, and CNC tools.
- Quality monitoring in food & beverage, packaging, and electronics.
4. EdgeAI Deployment
- Lightweight AI models optimized for edge devices (e.g., PLCs, industrial PCs, gateways).
- Low-latency inference for real-time decision-making without cloud dependency.
- Secure and scalable deployment across multiple production sites.
Technologies:
- TSLoom: Time-series optimization for industrial data.
- Federated Learning: Privacy-preserving model training across distributed sites.
- Containerized AI: Easy deployment via Docker/Kubernetes.
How It Works
- Data Collection: Collect real-time sensor data from PLCs, SCADA, or IoT devices.
- Model Training: Build custom ML models using TSLoom and AutoML for your specific process.
- Edge Deployment: Deploy models to edge devices for real-time inference and decision-making.
- Monitor & Improve: Refine models continuously with closed-loop feedback from production.
| Industry | Application |
|---|---|
| Automotive | Welding quality prediction, assembly line anomaly detection |
| Semiconductor | Wafer defect classification, equipment health monitoring |
| Metal & Machining | Tool wear prediction, surface defect detection |
| Electronics | PCB defect classification, soldering quality prediction |
| Food & Beverage | Packaging defect detection, process stability monitoring |
| Chemical & Pharma | Reaction optimization, batch process clustering |
Success Stories
- Roll-to-Roll Coating Process: Achieved 98% accuracy in predicting target coating thickness by analyzing process variables. Improved process performance by controlling variables using predictive models.
- Secondary Battery Manufacturer: Performed correlation analysis of process variables, identified root causes of anomalies, and developed visualization solutions for process optimization.
- Display Manufacturer: Enhanced process performance by analyzing display inspection images for defect detection, classification, and root-cause analysis.
- Display Equipment Manufacturer: Detected abnormal vibrations in equipment to identify faulty states and prevent downtime.
- Robot Application Manufacturer: Identified abnormal vibration segments in robot motion and improved process performance through motion optimization.
- Metal Machining Process: Monitored vibrations during CNC machining to classify processes and detect anomalous conditions.
Why Choose VCANUS?
✅ Industry-Specific Models: Pre-trained models for common manufacturing processes.
✅ Edge-to-Cloud Flexibility: Run AI on-premise, at the edge, or in the cloud.
✅ Seamless Integration: Compatible with Siemens, Rockwell, Beckhoff, and other industrial systems.
✅ Explainable AI: Transparent model outputs for operator trust and compliance.
✅ End-to-End Support: From data collection to model deployment and maintenance.
Get Started with VCANUS EdgeAI
Transform your manufacturing processes with data-driven intelligence. Contact us to:
- Discuss a pilot project tailored to your needs.
- Schedule a demo of our EdgeAI platform.
- Explore custom solution development for your unique challenges.