CYBER-PHYSICAL MANUFACTURING
of the next component. Additionally, these virtual twins exist on the cloud. So they can interact with other component twins that are geographically far away. Such models log the lifespan of components undergoing various stress levels and which function in different working regimes. This is one of the mechanisms through which the system will gain self-awareness. Machine Level: This level incorporates knowledge generated in the component level combined with machine operation history, system settings and so forth, to create an avatar for each machine. Virtual twins of similar machines are compared to each other as a way of identifying low-performance machines regardless of working regime. Fleet Level: The fact that virtual models are not bounded by time and location brings an opportunity to implement methods for modifying the production flow in response to changing conditions. For example, it is possible to optimize the way machines in the fleet handle production work through use of historical machine performance data and component status from component and machine levels. This method can be used to maximize the life span of all components while simultaneously keeping production and quality levels at their best points. The overall result is a system that is selfmaintaining and self-configuring. Enterprise Level: The highest level aggregates the outcomes of previous levels to produce a high-level performance report. This level can also incorporate optimization methods based on the needs of the enterprise. For example, certain enterprises might find it feasible to modify the production rate at one or more plants based on the fleet performance while keeping the total production rate and costs the same. CPSs store and maintain data in the cloud using standard formats. Use of standard formats lets developers create interactive web and mobile applications to present information to the users at different levels of the company. For example, a business executive needs information about throughput, production rate, supply chain management and so forth. He or she might need to see it on a smartphone during an international flight. In contrast, an engineer needs to see life-cycle management information and production quality estimates through a web interface inside the company. In one case, band-saw machines instrumented with sensors served as a demonstration of a CPS. The band-saw units were in different geographical locations. Sensors measured vibration, acoustics and pressure. The CPS also collected controller signals about factors such as feed rate and size of the material. On-site industrial computers performed preliminary data-to-information conversion. In the cloud, more complex adaptive use-based health analysis methods assessed machine performance and predicted wear in different machine components. Analysis results were available through web and mobile applications. These results are interesting, but they also reveal that many technologies—such as data storage systems, big data analysis methods, communication protocols and cyber-security—still need a lot of research, testing and development before the industrial IoT and CPSs can reach their potential.
This method can be used to maximize the life span of all components while simultaneously keeping production and quality levels at their best points.
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REFERENCES NSF I/UCRC for Intelligent Maintenance Systems (IMS), University of Cincinnati imscenter.net IDC, EMC, The Digital Universe of Opportunities: Rich Data and Increasing Value of the Internet of Things, 2014 emc.com/collateral/ analyst-reports/idc-digitaluniverse-2014.pdf Vijayaraghavan A, Sobel W, Fox A, Dornfeld D, Warndorf P., Improving machine tool interoperability using standardized interface protocols: MT connect, Lab Manuf Sustain, 2008. Chen G., Internet of Things towards Ubiquitous and Mobile Computing, Microsoft, 2010 research.microsoft.com/ en-us/um/redmond/events/ asiafacsum2010/presentations/ guihai-chen_oct19.pdf Bughin J, Chui M, Manyika J., An executive’s guide to the Internet of Things, McKinsey&Company, 2015 mckinsey.com/Insights/ Business_Technology/ An_executives_guide_to_the_ Internet_of_Things?cid=digitaleml-alt-mip-mck-oth-1508 Lee J, Bagheri B, Kao H-A., A Cyber Physical Systems Architecture for Industry 4.0-based Manufacturing System, Manuf Lett 2015;3:18–23. doi:10.1016/j. mfglet.2014.12.001. Yang S, Bagheri B, Kao H-A, Lee J., A Unified Framework and Platform for Designing of Cloud-Based Machine Health Monitoring and Manufacturing Systems, J Manuf Sci Eng 2015;137:040914. doi:10.1115/1.4030669.
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