Stanula, Patrick ; Ziegenbein, Amina ; Metternich, Joachim (2018)
Machine Learning Algorithms in Production: A Guideline for Efficient Data Source Selection.
In: Procedia CIRP, 6th CIRP Global Web Conference – Envisaging the Future Manufacturing, Design, Technologies and Systems in Innovation Era, Elsevier B.V., 78
Article
Abstract
Data acquisition, storage and processing becomes increasingly affordable and the use of machine learning algorithms feasible in the field of manufacturing. Even though state of the art machine tools are packed with sensors, the data’s benefits are difficult to assess in advance. Thus, this paper presents a management approach to select the most promising data sources regarding a defined objective. Quality Function Deployment matches the process specific objectives with preselected data sources. The preselection prevents the necessity to examine all possibilities while not restricting innovative solutions. This allows a targeted approach to fully exploit the advantages of machine learning. The approach is validated by a use case based on machine tool data.
| Item Type: | Article |
|---|---|
| Erschienen: | 2018 |
| Creators: | Stanula, Patrick ; Ziegenbein, Amina ; Metternich, Joachim |
| Type of entry: | Bibliographie |
| Title: | Machine Learning Algorithms in Production: A Guideline for Efficient Data Source Selection |
| Language: | English |
| Date: | 2018 |
| Journal or Publication Title: | Procedia CIRP, 6th CIRP Global Web Conference – Envisaging the Future Manufacturing, Design, Technologies and Systems in Innovation Era, Elsevier B.V. |
| Volume of the journal: | 78 |
| URL / URN: | https://doi.org/10.1016/j.procir.2018.08.177 |
| Abstract: | Data acquisition, storage and processing becomes increasingly affordable and the use of machine learning algorithms feasible in the field of manufacturing. Even though state of the art machine tools are packed with sensors, the data’s benefits are difficult to assess in advance. Thus, this paper presents a management approach to select the most promising data sources regarding a defined objective. Quality Function Deployment matches the process specific objectives with preselected data sources. The preselection prevents the necessity to examine all possibilities while not restricting innovative solutions. This allows a targeted approach to fully exploit the advantages of machine learning. The approach is validated by a use case based on machine tool data. |
| Uncontrolled Keywords: | machine learning, machine tool, quality function deployment, data source selection |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Management of Industrial Production |
| Date Deposited: | 14 Jan 2019 10:29 |
| Last Modified: | 14 Jan 2019 10:29 |
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