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 offers a video utilization system construction service using the video, FA, and AI collaboration platform 'FA Finder.' By linking various video information from manufacturing sites with IoT data and FA equipment, we can visualize data and associate and synchronize video information with events within the process, enabling monitoring of short stoppages, AI-based defect detection (anomaly detection), and evidence management. It can easily integrate with existing systems and FA equipment, allowing for gradual expansion, making it possible to build an automation system with a small start. 【Features】 ■ Utilizes a highly extensible video collaboration platform ■ Capable of integrating with various cameras and visualization tools ■ Achieves a low-latency network environment with proprietary video streaming technology ■ Our company, with extensive experience in supporting the development of PLCs and industrial robots, provides comprehensive support from camera selection to system construction. *For more details, please refer to the materials. Feel free to contact us with any inquiries.
Inquire About This Product
basic information
【Examples of Use】 ◎ Automating inspections that relied on the visual checks of skilled workers (reducing manpower and labor) ◎ Collaborating with devices that are difficult to analyze with AI due to lack of data for AI learning (improving production efficiency) ◎ Automating maintenance of manufacturing equipment (predictive maintenance) ◎ Integrating video information into BI tools to verify manufacturing data visually (increasing productivity) ◎ Enabling evidence management of manufacturing processes through video, proving that there is no fraud (building evidence) *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 detection system that identifies 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 | Using "FA Finder," we introduce traceability and evidence management systems that add "video information," ranging from simple to large-scale implementations. |
| Predictive Maintenance System for Equipment through AI Video Analysis | A case study focused on 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 judgment of skilled workers, was replaced by scanner-type line cameras and AI video analysis. |
catalog（6）
Download All Catalogs
Company information
Zuken Elmic is expanding its contract development based on its knowledge and achievements in communication protocols and low-latency video streaming. In the manufacturing sector, it is focusing on: - The platform 'FA Finder', which integrates 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.
![Collection of Questions and Suggestions from Automation Partners in Logistics Warehousing [Technical Document]](https://image.mono.ipros.com/public/product/image/3a1/2001066903/IPROS95321345586336081697.png?w=280&h=280)
