Jourdan, Nicolas ; Longard, Lukas ; Biegel, Tobias ; Metternich, Joachim
Hrsg.: Herberger, D. ; Hübner, M. (2021)
Machine Learning For Intelligent Maintenance And Quality Control: A Review Of Existing Datasets And Corresponding Use Cases.
2nd Conference on Production Systems and Logistics (CPSL 2021). online (10.08.2021-11.08.2021)
doi: 10.15488/11280
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The advent of artificial intelligence and machine learning is influencing the manufacturing industry profoundly, enabling unprecedented opportunities to improve manufacturing processes within the three dimensions time, quality and cost. With the introduction of digitization and industry 4.0, increasing amounts of data become available for processing and use in smart manufacturing systems. However, the various use cases for machine learning in manufacturing often require problem-specific datasets for training and evaluation of algorithms which are difficult to acquire, hindering both practitioners and academic researchers in this area. As the respective data frequently contains sensitive information, manufacturing companies rarely release datasets to the public. Further, the relevant attributes and features of available datasets are usually not evident, requiring time-consuming analysis to evaluate if a dataset fits a given problem. As a result, it can be challenging to develop and evaluate machine learning methods for manufacturing systems due to the lack of an overview of available datasets. This paper presents a comprehensive overview of 47 existing, publicly available datasets, mapped to various use cases in manufacturing with the goal of simplifying and stimulating research. The characteristics of the datasets are compared using a set of descriptive attributes to provide an outline and guidance for further research and application of machine learning in manufacturing. In addition, suitable performance metrics for the evaluation of classification use cases in manufacturing are presented.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Herausgeber: | Herberger, D. ; Hübner, M. |
| Autor(en): | Jourdan, Nicolas ; Longard, Lukas ; Biegel, Tobias ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Machine Learning For Intelligent Maintenance And Quality Control: A Review Of Existing Datasets And Corresponding Use Cases |
| Sprache: | Englisch |
| Publikationsjahr: | 10 August 2021 |
| Ort: | Hannover |
| Verlag: | Leibniz Universität Hannover |
| Buchtitel: | Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021) |
| Veranstaltungstitel: | 2nd Conference on Production Systems and Logistics (CPSL 2021) |
| Veranstaltungsort: | online |
| Veranstaltungsdatum: | 10.08.2021-11.08.2021 |
| DOI: | 10.15488/11280 |
| Kurzbeschreibung (Abstract): | The advent of artificial intelligence and machine learning is influencing the manufacturing industry profoundly, enabling unprecedented opportunities to improve manufacturing processes within the three dimensions time, quality and cost. With the introduction of digitization and industry 4.0, increasing amounts of data become available for processing and use in smart manufacturing systems. However, the various use cases for machine learning in manufacturing often require problem-specific datasets for training and evaluation of algorithms which are difficult to acquire, hindering both practitioners and academic researchers in this area. As the respective data frequently contains sensitive information, manufacturing companies rarely release datasets to the public. Further, the relevant attributes and features of available datasets are usually not evident, requiring time-consuming analysis to evaluate if a dataset fits a given problem. As a result, it can be challenging to develop and evaluate machine learning methods for manufacturing systems due to the lack of an overview of available datasets. This paper presents a comprehensive overview of 47 existing, publicly available datasets, mapped to various use cases in manufacturing with the goal of simplifying and stimulating research. The characteristics of the datasets are compared using a set of descriptive attributes to provide an outline and guidance for further research and application of machine learning in manufacturing. In addition, suitable performance metrics for the evaluation of classification use cases in manufacturing are presented. |
| Freie Schlagworte: | Data Analytics, Learning Factory, Machine learning, Problem Solving, Rework |
| 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) > CiP Center für industrielle Produktivität |
| Hinterlegungsdatum: | 06 Okt 2021 05:48 |
| Letzte Änderung: | 06 Jan 2022 14:25 |
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