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Machine Learning Algorithms in Production: A Guideline for Efficient Data Source Selection

Stanula, Patrick and Ziegenbein, Amina and 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., pp. 261-266, 78, ISSN 2212-8271, [Online-Edition: https://doi.org/10.1016/j.procir.2018.08.177],
[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 and Ziegenbein, Amina and Metternich, Joachim
Title: Machine Learning Algorithms in Production: A Guideline for Efficient Data Source Selection
Language: English
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.

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: 78
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 Management, Technology and Machine Tools (PTW)
16 Department of Mechanical Engineering > Institute of Production Management, Technology and Machine Tools (PTW) > Management of Industrial Production
Date Deposited: 14 Jan 2019 10:29
Official URL: https://doi.org/10.1016/j.procir.2018.08.177
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