Research data to "Digital assistance systems to implement Machine Learning in manufacturing: A systematic review"
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Date
2024-10-17
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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 based on the PRISMA-P process was conducted. Twenty-nine papers were identified and analyzed in depth regarding machine learning use case, required resources and research outlook. The available data show the procedure and explain how the authors arrived at the results of the accompanying paper.
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Except where otherwise noted, this license is described as CC BY-NC 4.0 - Attribution-NonCommercial 4.0 International