Interdisciplinary Data Driven Production Process Analysis for the Internet of Production. Meyes, R., Tercan, H., Thiele, T., Krämer, A., Heinisch, J., Liebenberg, M., Hirt, G., Hopmann, C., Lakemeyer, G., Meisen, T., & Jeschke, S. In Proceedings of the 46th North American Research Conference (NAMRC 46), pages 1065-1076, College Station, Texas, 2018.
Interdisciplinary Data Driven Production Process Analysis for the Internet of Production [link]Paper  abstract   bibtex   
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.
@inproceedings {LiebenbergBrain18,
        title = {Interdisciplinary Data Driven Production Process Analysis for the  Internet of Production},
        year = {2018},
        address = {College Station, Texas},
        author = {Richard Meyes and  Hasan Tercan and Thomas Thiele and Alexander
        Krämer and Julian Heinisch and Martin Liebenberg and Gerhard Hirt and Christian
        Hopmann and Gerhard Lakemeyer and Tobias Meisen and Sabina Jeschke},
        booktitle = {Proceedings of the 46th North American Research Conference (NAMRC 46)},
        pages = {1065-1076},
        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.},
        url = {https://www.sciencedirect.com/science/article/pii/S2351978918308199},
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021