Project duration: 01.01.2019 – 31.12.2020 | Project partners: Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich, German Machine Tool Builders' Association (VDW), Fraunhofer Institute for Applied and Integrated Security (AISEC) | Project homepage: https://www.mec.ed.tum.de/en/iwb/research-and-industry/projects/machine-tools/industry-40-for-machine-tools/
Objective
The Anonymization4Optimization project aims to design the anonymisation of machine data in such a way that it meets the requirements of both end customers/users and manufacturers. Suitable encryption and anonymisation methods for machine tool data are intended to enable a secure exchange of data along the value chain.
Results
To classify the sensitivity of machine data, suitable encryption and anonymisation methods for machine tool data were defined that meet the requirements of both end customers/users and manufacturers. In addition, different user groups were identified who are permitted to use the data on a time-limited basis. Verifying that the logs and data were free from manipulation was of decisive importance. Furthermore, a prototype "RatterAP" was implemented to determine the chatter behaviour of the machine tool. For this purpose, the necessary and sufficient data, including the sensor concept, were determined. This made it possible to define the control requirements and to develop a generally applicable method for implementing cloud services in machine tools. A cloud infrastructure was also set up with comprehensive consideration of IT security aspects. This included examining the potential of a private/public cloud by combining data from different machines in order to maximise efficiency and performance.
Industrial Benefit
Anonymization4Optimization enables manufacturing companies to securely exchange sensitive machine data while making it usable for optimisation purposes. The anonymisation methods developed build trust between machine manufacturers and users and form the basis for broader use of cloud services and data-driven process optimisation in manufacturing.
Significance
The project makes an important contribution to the secure digitalisation of the manufacturing industry. By developing standardisable anonymisation methods for machine tool data, it creates the basis for trustworthy, privacy-compliant networking of production systems. The results are transferable to other sectors and support the competitiveness of small and medium-sized manufacturers in the age of Industry 4.0.
Publications
Are you interested in data security in manufacturing or anonymised machine data for process optimisation? Contact us – together we will discuss how Anonymization4Optimization can optimise your manufacturing process.