Weber, Markus ; He, Fuzhang ; Weigold, Matthias ; Abele, Eberhard
Hrsg.: Behrens, Bernd-Arno ; Brosius, Alexander ; Hintze, Wolfgang ; Ihlenfeldt, Steffen ; Wulfsberg, Jens Peter (2021)
Development of a Temperature Strategy for Motor Spindles with Synchronous Reluctance Drive Using Multiple Linear Regression and Neural Network.
doi: 10.1007/978-3-662-62138-7
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
One central aspect of future high speed machining is the knowledge of the thermal behaviour of the machine tool and its motor spindle. A new temperature control for motor spindles with energy efficient synchronous reluctance drive is developed. In the first instance a finite element method model (FEM) is set up. This FEM aims to analyse the use case of nearly constant bearing temperatures within a defined range throughout the machining operation. By means of design of experiments (DOE), selected operating points of the speed-torque-characteristics are simulated with FEM considering different cooling parameters such as volume flow and inlet temperature. Machine learning algorithms are used to model the input-output-relationship in order to reduce the complex thermal motor spindle FEM. The applied algorithms are multiple linear regression and artificial neural network. The concept of temperature strategy, the FEM simulation results and the thermal models using machine learning algorithms are presented.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Herausgeber: | Behrens, Bernd-Arno ; Brosius, Alexander ; Hintze, Wolfgang ; Ihlenfeldt, Steffen ; Wulfsberg, Jens Peter |
| Autor(en): | Weber, Markus ; He, Fuzhang ; Weigold, Matthias ; Abele, Eberhard |
| Art des Eintrags: | Bibliographie |
| Titel: | Development of a Temperature Strategy for Motor Spindles with Synchronous Reluctance Drive Using Multiple Linear Regression and Neural Network |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Ort: | Berlin |
| Verlag: | Springer |
| Buchtitel: | 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 |
| Reihe: | Lecture Notes in Production Engineering |
| DOI: | 10.1007/978-3-662-62138-7 |
| Kurzbeschreibung (Abstract): | One central aspect of future high speed machining is the knowledge of the thermal behaviour of the machine tool and its motor spindle. A new temperature control for motor spindles with energy efficient synchronous reluctance drive is developed. In the first instance a finite element method model (FEM) is set up. This FEM aims to analyse the use case of nearly constant bearing temperatures within a defined range throughout the machining operation. By means of design of experiments (DOE), selected operating points of the speed-torque-characteristics are simulated with FEM considering different cooling parameters such as volume flow and inlet temperature. Machine learning algorithms are used to model the input-output-relationship in order to reduce the complex thermal motor spindle FEM. The applied algorithms are multiple linear regression and artificial neural network. The concept of temperature strategy, the FEM simulation results and the thermal models using machine learning algorithms are presented. |
| Freie Schlagworte: | Temperature strategy, Motor spindles, Machine learning |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > Werkzeugmaschinen und Komponenten (2021 aufgegangen in TEC Fertigungstechnologie) |
| Hinterlegungsdatum: | 16 Dez 2020 06:26 |
| Letzte Änderung: | 29 Dez 2020 09:46 |
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