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Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Barton, D.; Fleischer, J.

Vibrations have a significant influence on quality and costs in metal
cutting processes. Existing methods for predicting vibrations in machine tools enable an informed choice of process settings, however they rely on costly equipment and specialised staff. Therefore, this contribution proposes to reduce the modelling effort required by using machine learning based on data gathered during production. The approach relies on two sub-models, representing the machine structure and machining process respectively. A method is proposed for initialising and updating the models in production.

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Postprint §
DOI: 10.5445/IR/1000124053
Veröffentlicht am 26.09.2021
DOI: 10.1007/978-3-662-62138-7_55
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Buchaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISBN: 978-3-662-62138-7
ISSN: 2194-0525, 2194-0533
KITopen-ID: 1000124053
Erschienen in Production at the leading edge of technology – Proceedings of the 10th Congress of the German Academic Association for Production Technology (WGP), Dresden, 23-24 September 2020. Ed.: B.-A. Behrens
Verlag Springer
Seiten 549–558
Serie Lecture Notes in Production Engineering
Vorab online veröffentlicht am 25.09.2020
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