Easily integrate camera footage with existing systems and FA equipment. It can be gradually expanded from monitoring chocolate stops to AI-based defect detection and evidence management.
Our company provides a video utilization system construction service using the video, FA, and AI collaboration platform 'FA Finder.' By linking various video information from the manufacturing site with IoT data and FA equipment, we can visualize data and associate and synchronize video information with events in the process, enabling monitoring of short stops, AI-based defect detection (anomaly detection), and evidence management. It can easily integrate with existing systems and FA equipment, allowing for gradual expansion, so you can build an automation system starting small. 【Features】 ■ Utilizes a highly extensible video collaboration platform ■ Compatible with various cameras and visualization tools ■ Achieves low-latency network environments with proprietary video streaming technology ■ Our company, with extensive experience in supporting PLC and industrial robot development, provides comprehensive support from camera selection to system construction *For more details, please refer to the materials. Feel free to contact us as well.
Inquire About This Product
basic information
【Examples of Use】 ◎ Automation of inspections that relied on the visual checks of skilled workers (labor-saving and efficiency) ◎ Collaboration with devices that are difficult to analyze with AI due to lack of data for AI learning (improving production efficiency) ◎ Automation of maintenance for manufacturing equipment (predictive maintenance) ◎ Integration of video information into BI tools to verify manufacturing data visually (increased productivity) ◎ Enabling evidence management of manufacturing processes through video, proving that there is no fraud (evidence construction) *For more details, please refer to the materials. Feel free to contact us with any inquiries.
Price range
Delivery Time
Applications/Examples of results
- Hybrid imaging-based cause analysis system for defects occurring in high-speed production lines - Traceability system for vehicle inspection processes utilizing video - Defect inspection system that detects fine weaving irregularities in fabric using line cameras and AI image analysis, providing results to end users - Predictive maintenance system that detects maintenance timing for sprayers from images of spray holes *For more details, please refer to the materials. Feel free to contact us with any inquiries.*
Line up（4）
| Model number | overview |
|---|---|
| Chocolate Stop Cause Analysis System through Hybrid Imaging | Synchronously playing back high-speed cameras and surveillance cameras to reliably capture the moment and cause of chocolate stops. |
| Traceability and Evidence Management System Utilizing Video Starting with a Small Start | Introducing traceability and evidence management systems built by adding "video information" using "FA Finder," ranging from simple to large-scale. |
| Predictive Maintenance System for Equipment through AI Video Analysis | A case study working towards realizing a "non-stop factory" and reducing the manpower required for maintenance! |
| Inspection System using Scanner-type Cameras and AI | A case where inspection, which relied on the visual checks of skilled workers, was replaced by scanner-type line cameras and AI video analysis. |
catalog（1）
Download All Catalogs
Company information
Zuken Elmic is expanding its contract development based on its expertise and achievements in communication protocols and low-latency video streaming. In the manufacturing sector, it is focusing on: - The platform 'FA Finder', which links video, AI, FA equipment, and visualization systems for FA to build systems for detecting product defects and equipment malfunctions, as well as traceability systems. - Industrial network implementation support services.
![Crane Load Lowering Safety Measures "Sky Watcher" [NETIS Registered]](https://image.www.ipros.com/public/product/image/d85/2000958789/IPROS5302643829032324147.jpg?w=280&h=280)
