While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.
Kurzfassung
Die Technologien von Industrie 4.0 bieten global produzierenden Unternehmen die Chance, Entscheidungen zur Netzwerkgestaltung zu verbessern und die Steuerung der Produktion weltweit zu synchronisieren. Dadurch werden eine nachhaltige Steigerung des Wertschöpfungsgrads in der Produktion und die Nutzung von Kostenvorteilen ermöglicht. In der Praxis zeigt sich jedoch, dass die Umsetzung ein langer Weg ist. Durch die Unterteilung der Vision eines transparenten und agilen Netzwerks in gestaffelte und parallelisierte Teilprojekte lässt sich jedoch auf allen Arbeitsebenen direkter Mehrwert für Mitarbeiter und Unternehmen erzielen, der gleichzeitig zur Gesamtentwicklung beiträgt. Der vorliegende Beitrag zeigt hierzu konkrete Schritte aus den Bereichen Strategie, Gestaltung und Management von Produktionsnetzwerken auf.
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