Interdisciplinary Data Driven Production Process Analysis for the Internet of Production

Abstract Recent developments in the industrial field are strongly influenced by requirements of the fourth industrial revolution (I4.0) for modern Cyber-Physical Production Systems (CPPS) and the coherent phenomenon of industrial big data (IBD). I4.0 is characterized by a growing amount of interdisciplinary work and cross-domain exchange of methods and knowledge. Similar to the development of the Internet of Things (IoT) for the consumer market, the emergence of an Internet of Production (IoP) in the industrial field is imminent. The future vision for an IoP is based on aggregated, multi-perspective and persistent data sets that can be seamlessly and semantically integrated to allow diagnosis and prediction in domain-specific real-time. In this paper, we demonstrate an exemplary scenario of collaborative cross-domain work, in which domain-experts from largely different fields of expertise, i.e. heavy plate rolling (HPR), injection molding (IM) and machine learning (ML), generate insights through data driven process analysis in two use cases. Specifically, in the HPR use case, reinforcement-learning was utilized to support the planning phase of the process aiming to reduce manual work load and to ultimately generate process plans that serve as a foundation for a simulation to calculate process results. On the contrary, in the IM use case, supervised-learning was utilized to learn a complex and computationally demanding finite element simulation model in order to predict process results for unknown process configurations, which can be used to optimize the process planning phase. While both use cases had the overall goal to utilize ML to gain new insights about the respective process, the actual ML application was utilized with reversed purpose. Particularly, in the HPR use case, ML was used to learn the process planning in order to calculate process results while in the IM use case, ML was used to predict process results in order to improve the process planning. We facilitate the communication between physically separated domain experts and the exchange of gained insights in the respective use cases by a framework that addresses the specific needs of cross-domain collaboration. We show that the insights gained from two largely different use cases are valuable to the domain experts of the other respective use case, facilitating cross-domain data driven production process analysis for future IoP scenarios.