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Utilizing Data Analysis in Robotic Systems

by GO ON
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In the era of digitalization and Industry 4.0, data has become a key element of success in most industrial sectors. This is particularly true in the field of robotics, where effective data mining can mean the difference between successful operation and production downtime. But what exactly does data mining mean for robotics?

Experts in robotics, robot manufacturers, mechanical engineers, and end-users have the most valuable knowledge about their systems. Data mining is a process that allows these experts to gain valuable information from extensive datasets. Data mining involves the collection, analysis, and interpretation of data, enabling them to better understand operational conditions and perform effective analyses.

SCOPE – A Data-Driven Solution Supported by Expertise

SCOPE is more than just a data mining tool. Its open data interface allows easy connectivity with other MES (Manufacturing Execution System) or ERP (Enterprise Resource Planning) systems. This enables collaboration between various stakeholders and real-time data sharing.

The SCOPE solution utilizes a decentralized Edge system that ensures users retain ownership of their data. Open data routing functions through HTTP, WebSocket, or MQTT protocols offer full connectivity to any compatible MES or ERP layer for further local exploration or tasks such as email or SMS notifications.

The SCOPE system enables the connection of up to 50 robots into a single functional unit. This innovative hardware platform creates a bridge between robots and various system layers. As a result, users can better coordinate and manage their robotic fleet.

SCOPE continuously analyzes the status and load of robots, alerts upcoming maintenance, and offers an overview of production resources for better control of the overall equipment effectiveness (OEE). This proactive approach ensures problems are detected in advance, before they become an issue, helping manufacturing enterprises achieve more efficient and smarter processes and moving the entire operation a step closer to zero downtime.

In its short time on the market, SCOPE has already gained a number of references. One such case involved an initially inexplicable problem of irregular production of defective parts on a line. An analysis of the force data deviation from the average value during the placement of parts onto the carrier revealed that all affected parts were produced on the same carrier, which was bent.

An example of data mining usage: Data analysis from production revealed that the problem repeatedly occurred with parts manufactured on the same carrier. Identifying similar issues can also use the carrier’s identification number (e.g., barcode reader), product serial number (e.g., production tracking), product quality feedback (in the simplest case, good/bad part, e.g., control camera), or the aforementioned deviation from the average value of force when placing parts on the carrier (e.g., reading the robot motor’s current).

Using the autonomous SCOPE system for data mining is an essential step in gaining valuable insights from data, allowing industrial experts to better understand their systems and perform effective analyses. This achieves higher productivity, efficiency, and quality of production, which is the path to success in the modern industry.

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