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Machine Learning Based Identification of Energy Efficiency Measures for Machine Tools Using Load Profiles and Machine Specific Meta Data

Petruschke, Lars ; Elserafi, Ghada ; Ioshchikhes, Borys ; Weigold, Matthias (2021):
Machine Learning Based Identification of Energy Efficiency Measures for Machine Tools Using Load Profiles and Machine Specific Meta Data.
In: MM Science Journal, 2021 (5), pp. 5061-5068. ISSN 1803-1269,
DOI: http://doi.org./10.17973/MMSJ.2021_11_2021153,
[Article]

Abstract

Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.

Item Type: Article
Erschienen: 2021
Creators: Petruschke, Lars ; Elserafi, Ghada ; Ioshchikhes, Borys ; Weigold, Matthias
Title: Machine Learning Based Identification of Energy Efficiency Measures for Machine Tools Using Load Profiles and Machine Specific Meta Data
Language: English
Abstract:

Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.

Journal or Publication Title: MM Science Journal
Journal volume: 2021
Number: 5
Uncontrolled Keywords: Energy efficiency measures, energy states, expert system, machine tool
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW)
Date Deposited: 05 Nov 2021 07:14
DOI: http://doi.org./10.17973/MMSJ.2021_11_2021153
Additional Information:

Special Issue: HSM 2021, 16th International Conference on High Speed Machining, October 26-27, 2021, Darmstadt, Germany

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