Version 1
: Received: 7 October 2024 / Approved: 7 October 2024 / Online: 7 October 2024 (17:33:39 CEST)
How to cite:
Rosemeyer, J.; Pinzone, M.; Metternich, J. Digital Assistance Systems to Implement Machine Learning in Manufacturing: Current State and Future Directions. Preprints2024, 2024100522. https://doi.org/10.20944/preprints202410.0522.v1
Rosemeyer, J.; Pinzone, M.; Metternich, J. Digital Assistance Systems to Implement Machine Learning in Manufacturing: Current State and Future Directions. Preprints 2024, 2024100522. https://doi.org/10.20944/preprints202410.0522.v1
Rosemeyer, J.; Pinzone, M.; Metternich, J. Digital Assistance Systems to Implement Machine Learning in Manufacturing: Current State and Future Directions. Preprints2024, 2024100522. https://doi.org/10.20944/preprints202410.0522.v1
APA Style
Rosemeyer, J., Pinzone, M., & Metternich, J. (2024). Digital Assistance Systems to Implement Machine Learning in Manufacturing: Current State and Future Directions. Preprints. https://doi.org/10.20944/preprints202410.0522.v1
Chicago/Turabian Style
Rosemeyer, J., Marta Pinzone and Joachim Metternich. 2024 "Digital Assistance Systems to Implement Machine Learning in Manufacturing: Current State and Future Directions" Preprints. https://doi.org/10.20944/preprints202410.0522.v1
Abstract
Implementing machine learning technologies in manufacturing environment relies heavily on human expertise in terms of domain and machine learning knowledge. Yet, the required machine learning knowledge is often not available in manufacturing companies. A possible solution to overcome this competence gap and let domain experts with limited machine learning programming skills build viable applications are digital assistance systems that support the implementation. At the present, there is no comprehensive overview over corresponding assistance systems. Thus, within this study a systematic literature review was conducted. Twenty-nine papers were identified and analyzed in depth regarding machine learning use case, required resources and research outlook. Six key findings as well as requirements for future developments are derived from the investigation. As such, the existing assistance systems basically focus on technical aspects whereas the integration of the users as well as validation in industrial environments lack behind. Future assistance systems should put more emphasis on the users and integrate them both in development and validation.
Keywords
machine learning; systematic literature review; manufacturing; digital assistance systems; work-based learning
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
