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 optimize 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.
Increasing the life of cutting tools through transferable AI, reducing process costs
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