InnoLog: 10th Mechanical Engineering Day Thüringen 2026
Machine-Learning-gestütztes Monitoringsystem zur Zustandsüberwachung von Zerspanwerkzeugen in CNC-Maschinen (A machine-learning-based monitoring system for condition monitoring of cutting tools in CNC machines)
Wolfram Kattanek
Abstract:
In the session ‘Automation of secondary processes: material flow, logistics, assistance systems, maintenance’, a short pitch presentation will introduce an AI-supported tool monitoring system currently being developed as part of the ZIM project ZEMOS and intended for use in milling processes on machine tools. By combining high-resolution sensor technology, local machine learning analysis and latency-optimised data processing, the system aims to enable real-time diagnosis of tool wear in CNC machines. This will help to reduce unplanned machine downtime, improve production quality, make better use of the actual tool life and thus lower overall production costs. To ensure the solution is widely applicable, it will be possible to retrofit it onto existing machines.
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Lead application
Adaptive edge AI systems for industrial application
We are researching solutions for adaptive edge AI systems to make AI possible on low-consumption embedded systems in industry and to network them in real time.
Service for R&D
Development of embedded systems
We develop embedded systems for you as complete solutions consisting of sensors and actuators, signal processing and communications technology as well as open-loop and closed-loop controls.
Core topic
Embedded AI
The numerous existing AI algorithms and methods for high-performance computing are unsuitable for embedded systems. We are researching to optimise AI algorithms and methods so that they can be used on embedded systems.
Research field
Smart distributed measurement and test systems
Integrated sensor ICs are the heart of sensor and measurement systems like wireless sensors, stationary or handheld devices. We are researching solutions for ever more powerful sensors with more intrinsic intelligence and task allocation in the network.
Contact
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