GF Machining Solutions' rConnect Dasboard Collects, Compares Shop Data
GF Machining Solutions has introduced rConnect, a digital services platform powered by Open Platform Communications Unified Architecture (OPC UA) protocol.
GF Machining Solutions has introduced rConnect, a digital services platform powered by the Open Platform Communications Unified Architecture (OPC UA) protocol. The system is said to help manufacturers reduce machine downtime, monitor the performance of their machines, automate their processes, ensure process compliance, and connect their GF Machining Solutions machines to enterprise resource planning (ERP) systems and manufacturing execution systems (MES).
The OPC UA protocol provides a common communication language across GF Machining Solutions’ products, technologies and services. Version one of the OPC UA collects a range of comparable data and, with the rConnect Dashboard, makes it accessible on the shop floor as well as remotely. OPC UA also allows users to connect GF Machining Solutions products to their existing ERP and MES systems.
Following collection, the rConnect Dashboard makes a range of live data available related to key performance indicators (KPIs). All of this data is comparable and structurally aligned with ISO 22400’s framework for defining, composing, exchanging and using KPIs in manufacturing. Machine status, availability, efficiency and productivity are all tracked, enabling users to easily monitor machine activity and receive alerts about issues such as deviations from normal productivity.
While OPC UA and MTConnect are both http-based protocols, there are differences between them, and each is best used in differing scenarios.
An MTConnect-enabled monitoring system gives this shop a clear and simple picture of machine tool usage.
A panel discussion at the recent Top Shops Conference focused on various points of view regarding the value of connecting machine tools to a network for monitoring performance and recording results. Because machine monitoring helps a shop make better decisions about manufacturing processes, it is a good example of data-driven manufacturing in action.