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A maturity model for digital ML tools to be used in manufacturing environments

Rosemeyer, Jannik ; Neunzig, Christian ; Akbal, Cem ; Metternich, Joachim ; Kuhlenkötter, Bernd (2024)
A maturity model for digital ML tools to be used in manufacturing environments.
doi: 10.26083/tuprints-00026519
Report, Primary publication, Publisher's Version

Abstract

Low or no code machine learning platforms, whereof tools such as KNIME, DataRobot or WEKA are among the best-known, have facilitated the implementation of machine learning applications in industrial environments in recent years by transferring programming tasks to an assistance system instead of demanding users to provide the respective skills. Despite the high number of innovations, to the best of the authors’ knowledge, there is no comprehensive classification scheme to assess the autonomy of those tools. Hence, this paper demonstrates a maturity model that classifies the assistance level of existing digital machine learning tools with respect to the requirements of manufacturing environments. It is based on the levels of driving automation and concretized by the so-called CRISP-ML(Q) procedure model. The model allows researchers to rate newly developed tools against existing ones and aims to serve as a baseline for future research. To evaluate the added value to the research landscape, semi-structured interviews with four ML experts were conducted. Finally, five commercial tools were categorized in the model to show its applicability.

Item Type: Report
Erschienen: 2024
Creators: Rosemeyer, Jannik ; Neunzig, Christian ; Akbal, Cem ; Metternich, Joachim ; Kuhlenkötter, Bernd
Type of entry: Primary publication
Title: A maturity model for digital ML tools to be used in manufacturing environments
Language: English
Date: 15 January 2024
Place of Publication: Darmstadt
Collation: 9 ungezählte Seiten
DOI: 10.26083/tuprints-00026519
URL / URN: https://tuprints.ulb.tu-darmstadt.de/26519
Abstract:

Low or no code machine learning platforms, whereof tools such as KNIME, DataRobot or WEKA are among the best-known, have facilitated the implementation of machine learning applications in industrial environments in recent years by transferring programming tasks to an assistance system instead of demanding users to provide the respective skills. Despite the high number of innovations, to the best of the authors’ knowledge, there is no comprehensive classification scheme to assess the autonomy of those tools. Hence, this paper demonstrates a maturity model that classifies the assistance level of existing digital machine learning tools with respect to the requirements of manufacturing environments. It is based on the levels of driving automation and concretized by the so-called CRISP-ML(Q) procedure model. The model allows researchers to rate newly developed tools against existing ones and aims to serve as a baseline for future research. To evaluate the added value to the research landscape, semi-structured interviews with four ML experts were conducted. Finally, five commercial tools were categorized in the model to show its applicability.

Uncontrolled Keywords: machine learning; digital assistance systems; autonomy levels, levels of driving automation
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-265198
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 670 Manufacturing
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) > CiP Center for industrial Productivity
Date Deposited: 15 Jan 2024 13:08
Last Modified: 16 Jan 2024 07:24
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