Application Scenarios of a Tactile Surface Roughness Measurement System for In Situ Measurement in Machine Tools

The rate of automation in European industry is increasing continuously. In production metrology, the trend is shifting from measurement laboratories towards integration of metrology into the production process. Increasing levels of automation and the current skills shortage are driving demand for autonomous production systems. In this project, a roughness measurement system was developed that is fully integrated into machine tools and enables fully automatic roughness measurement of part surfaces during the machining process. Using a skidless measurement system, it was possible to obtained measured roughness values comparable to those obtained in measuring rooms under optimal conditions. The present paper shows the development process of the prototype and provides an overview of different application scenarios for in situ measurement of machine tools. In situ roughness measurement has high potential in the future of metrology in industrial applications. Not only can surfaces be measured directly in the process, sub-processes can be triggered based on the measured values, allowing the production process to react flexibly to actual conditions. Potential improvements in metrology and significant optimizations of the entire production chain are highlighted in this paper

Fleet learning of thermal error compensation in machine tools

Thermal error compensation of machine tools promotes sustainable production. The thermal adaptive learning control (TALC) and machine learning approaches are the required enabling principals. Fleet learnings are key resources to develop sustainable machine tool fleets in terms of thermally induced machine tool error. The target is to integrate each machine tool of the fleet in a learning network. Federated learning with a central cloud server and dedicated edge computing on the one hand keeps the independence of each individual machine tool high and on the other hand leverages the learning of the entire fleet. The outlined concept is based on the TALC, combined with a machine agnostic and machine specific characterization and communication. The proposed system is validated with environmental measurements for two machine tools of the same type, one situated at ETH Zurich and the other one at TU Wien

EuProGigant – A Concept Towards an Industrial System Architecture for Data-Driven Production Systems

Today, most IoT solutions for the production ecosystem stem from trends that first established at the consumer market. Although these concepts have been adapted well in the industrial environment, it led to fragmented solutions that require complex interfaces. With the recent introduction of GAIA-X, it becomes possible to develop platform independent IoT solutions tailored to the needs of manufacturers. Until now, GAIA-X is only a concept proposed by governments and economic advisory boards. This paper extends the concept into an industrial system architecture that enables the reliable exchange of information among the supply chain of a highly distributed production network

Cloud-based thermal error compensation with a federated learning approach

Thermal error compensation is one of the most research-oriented topics in manufacturing with rising importance in the industry. This paper presents an innovative Industry 4.0 application of thermal error compensation for precision engineering. A federated learning-based thermal error compensation approach running in the cloud is applied to two machine tools, one located at ETH Zürich, and another one at TU Wien. Although environmental conditions and thermal error behaviour of both machines differ, the implemented knowledge transfer across machines is a viable compensation strategy, albeit with limited precision. A detailed comparison of the two machines of the same type under the same load conditions shows foreseeable similarities in behaviour, but also clear differences due to the different configurations and lifetime status. The cloud-based compensation reduced the crucial thermal errors in the best case of both machine tools by more than 80% under critical conditions