Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability
This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/3/153 |
| _version_ | 1797240238340308992 |
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| author | Alexander Bott Simon Anderlik Robin Ströbel Jürgen Fleischer Andreas Worthmann |
| author_facet | Alexander Bott Simon Anderlik Robin Ströbel Jürgen Fleischer Andreas Worthmann |
| author_sort | Alexander Bott |
| collection | DOAJ |
| description | This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes. |
| first_indexed | 2024-04-24T18:04:15Z |
| format | Article |
| id | doaj.art-e4f67142cf3d4885bb5d9711965153fc |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-04-24T18:04:15Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-e4f67142cf3d4885bb5d9711965153fc2024-03-27T13:51:47ZengMDPI AGMachines2075-17022024-02-0112315310.3390/machines12030153Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and SustainabilityAlexander Bott0Simon Anderlik1Robin Ströbel2Jürgen Fleischer3Andreas Worthmann4wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germanywbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germanywbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germanywbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, GermanyInstitute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Seidenstrasse 36, 70174 Stuttgart, GermanyThis study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes.https://www.mdpi.com/2075-1702/12/3/153machine toolsonline optimizationenergy efficiencytool wearsurface qualityasset administration shell |
| spellingShingle | Alexander Bott Simon Anderlik Robin Ströbel Jürgen Fleischer Andreas Worthmann Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability Machines machine tools online optimization energy efficiency tool wear surface quality asset administration shell |
| title | Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability |
| title_full | Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability |
| title_fullStr | Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability |
| title_full_unstemmed | Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability |
| title_short | Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability |
| title_sort | framework for holistic online optimization of milling machine conditions to enhance machine efficiency and sustainability |
| topic | machine tools online optimization energy efficiency tool wear surface quality asset administration shell |
| url | https://www.mdpi.com/2075-1702/12/3/153 |
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