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Nachrichten, Termine, Experten
Nachrichten, Termine, Experten
Two factors influence the production costs of a machined component: The volume of material removed over time and tool wear. In order to reliably predict the state of wear and thus optimise cutting processes, researchers at Technische Universität Kaiserslautern (TUK) are developing a process supported by artificial intelligence (AI). They will be presenting their concept at the Hannover Messe from 30 May to 3 June at the Rhineland-Palatinate research stand (Hall 2, Stand B40). The system will be trained using real process and measurement data. The aim is to adapt the system to different process conditions by means of transfer learning.
Tools used for machining processes in manufacturing, such as drilling or milling, wear out over their period of use. Companies usually replace them prematurely after an experience-based number of operations in order to avoid quality losses or even expensive downtimes due to tool breakages. But replacing tools is also costly in terms of time and money, so it is ultimately worthwhile to optimise the replacement cycles.
“In order to be able to predict the state of wear of cutting tools, we first take process-related parameters into account,” says Daniel Müller, research associate at the Institute for Manufacturing Technology and Production Systems at TUK. These include the process forces acting during cutting, vibrations and the power requirements of the machine axes. “Likewise, we collect data from continuous measurements taken on the tool and the workpiece,” says the engineer. “The biggest challenge is then to determine correlations in the collected data.”
For this purpose, the researchers train an AI-supported system that uses machine learning methods to recognise possible patterns and derive conclusions on the wear condition. In addition, the system is supposed to be able to predict which process parameters companies have to work with in certain machining processes in order to keep the tool in use safely and reliably for a target service life. “The data that the system needs to learn is collected from five partner companies - including global players as well as small and medium-sized enterprises,” explains Daniel Müller. “In doing so, we test a wide range of variants, such as tool and material types or process parameters, and thus collect a broad data base over the entire tool life up to the failure of the tool.
These results are used to develop an adaptable basic model that is adaptable. “We use the concept of transfer learning,” reports Peter Simon, who is working on the project together with Daniel Müller. “This approach is to allow the basic model to be adapted to customer-specific machining processes with as little additional effort as possible. We will moreover exploit the findings of the research project within the framework of this utilisation in the form of innovative business models.”
At the Hannover Messe, the researchers will provide interested companies with in-depth insights into their research work.
The research project entitled „Beherrschung von Zerspanprozessen durch transferierbare künstliche Intelligenz – Grundlage für Prozessverbesserungen und neue Geschäftsmodelle“ (TransKI) [Mastering machining processes through transferable artificial intelligence - basis for process improvements and new business models] is funded by the Federal Ministry of Education and Research (BMBF) within the framework of the funding programme „Lernende Produktion - Einsatz Künstlicher Intelligenz (KI) in der Produktion (ProLern)“ [Learning Production - Use of Artificial Intelligence (AI) in Production] (FKZ: 02P20A031).
Questions can be directed to:
Peter Simon, M.Sc.
Institute for Manufacturing Technology and Production Systems
Phone: +49 631 205-4210
E-mail: peter.simon@mv.uni-kl.de
Dipl.-Ing. Daniel Müller
Institute for Manufacturing Technology and Production Systems
Phone: +49 631 205-3385
E-mail: daniel.mueller@mv.uni-kl.de
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Klaus Dosch, Department of Technology and Innovation, is organizing the presentation of the researchers of the TU Kaiserslautern at the fair. He is the contact partner for companies and, among other things, establishes contacts to science.
Contact: Klaus Dosch, Email: dosch[at]rti.uni-kl.de, Phone: +49 631 205-3001
Peter Simon, M.Sc.
Institute for Manufacturing Technology and Production Systems
Phone: +49 631 205-4210
E-mail: peter.simon@mv.uni-kl.de
Dipl.-Ing. Daniel Müller
Institute for Manufacturing Technology and Production Systems
Phone: +49 631 205-3385
E-mail: daniel.mueller@mv.uni-kl.de
Daniel Müller (left), Maximilian Berndt and Peter Simon (right) are working in their research projec ...
Credit: view/Reiner Voss / TUK
TUK
Merkmale dieser Pressemitteilung:
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Informationstechnik, Maschinenbau, Werkstoffwissenschaften
überregional
Forschungs- / Wissenstransfer, Forschungsprojekte
Englisch
Daniel Müller (left), Maximilian Berndt and Peter Simon (right) are working in their research projec ...
Credit: view/Reiner Voss / TUK
TUK
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