n TECHNOLOGY January 2025 www.drivesncontrols.com 18 WEIDMULLER HAS ANNOUNCED A machine learning (ML) tool that allows you to implement ML at a machine, without needing to connect to the cloud or the Internet. The edgeML tool can run learning algorithms at the edge on a PLC or industrial PC. The software is also available as a Docker container, allowing it to be used on any industrial controller that can handle these containers. This manufacturer-independent capability can execute various machine learning models. A no-code design makes it possible to deploy ML models on controllers without needing any knowledge of Python or data science. According to Weidmüller, machine learning directly at a system offers numerous advantages, including the local collection, storage and processing of data. Unlike cloud-based ML systems, the machine or controller does not need to transfer the data to the cloud, ensuring that sensitive data does not leave a company. Any discrepancies in production processes are detected at the machine. This speeds up troubleshooting, prevents prolonged downtimes, and reduces the production of rejects, Weidmüller says. At the same time, edgeML cuts costs because it eliminates the need for cloud licences, as well as fees for data transmission and storage. Production lines with machines and systems that cannot be connected to the Internet for security reasons can also benefit from local ML capabilities. The path to a machine learning at the edge begins by importing data from the system into ModelBuilder software, where users can create ML models based on the data. These models are then transferred to edgeML. Because edgeML supports the standard ONNX format, users can create models other than ModelBuilder – using Python, for example. This allows them to implement ML in a familiar environment. If a model is no longer performing as desired, it can be replaced easily without having to adjust any communications settings. In this way, edgeML make it easier to manage the lifecycles of ML models. To minimise the use of time and resources when creating ML solutions, Weidmüller plans to allow the calibration of created models in the future. This is already available in ModelRuntime. A standard model for a machine family thus becomes a template that can be extended to other machines of the same class. The applied model continues to learn from these machines to adapt to the system, enabling the ML models to be scaled and re-used as needed. For its next stage of development, Weidmüller plans to further improve the accessibility of edgeML. A connector will overcome the boundaries of fieldbuses and protocols, making it usable on any system. www.weidmueller-gti-software.com Tool allows machine learning at a machine, avoiding the cloud .. edgeML makes machine learning possible where the data is generated SKF HAS TEAMED UP with the machine tool manufacturer DMG Mori to use SKF’s Insight Super-precision bearing system to push the performance of machine tool spindles, while maintaining or improving their reliability. The system measures the loads, speeds, temperatures and vibrations experienced by a bearing in real time, and fits in the same space as a conventional bearing. It is accurate, fast and robust and can be applied to any rolling element bearing to enhance the performance of machines or processes. In a joint programme, SKF and DMG Mori will develop and validate the technology in machining centres. Teams from both companies will test the system to ensure that it meets the performance requirements of the machine tool industry. The system will allow DMG Mori to improve lubrication control, enhancing bearing lives and reliability, as well as facilitating precision machining. These improvements could lead to higher productivity and enhanced workpiece quality. Dr Naruhiro Irino, director of DMG Mori’s Advanced Technology Research Centre, says the development will allow real-time monitoring of bearing temperatures, loads, and vibration during machining, “offering insights that were previously beyond reach.” Annika Olme, CTO and senior vicepresident of Technology Development at SKF, predicts that the collaboration “will unlock new technology benefits for customers and end-users”. The Insight technology has already been proven in a variety of industries and applications, integrating intelligence without compromising the compactness or stiffness performance of SKF’s Superprecision bearings. It can help to cut noise levels and heat generation. The bearings are suitable for other applications, such as high-speed finishing stands for steel wire, and engines and transmissions in racing cars. www.skf.com SKF and DMG Mori join forces on a bearing monitoring technology for machine tools
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