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46 SMT007 MAGAZINE I JUNE 2019 turing fundamentals such as DMAIC, can be used to approach the improvement effort in a methodical manner. DMAIC is an acronym which defines the phases of the improvement process: Define, Measure, Analyze, Improve, Control. So before we can improve the poor utilization, we must measure and analyze the current state. Johnson: Were you considering CFX when you wrote this paper? Does the presence of CFX change your perspectives on a universal lan- guage? Why or why not? Elliott: CFX is a universal communication proto- col operating at the lowest level of the three-tier smart factory architecture. For PCB assembly, technologies like CFX and the Open Manufac- turing Language (OML) provide a solution to the problem of machine-to-machine commu- nication on the shop floor. This has been a major hurdle in the past, but now we are look- ing at the bigger picture: What to do with all of this data being generated? How to classify and make use of information coming from dis- parate production processes? When and what action to take based on the data collection? Our machines, systems, and people must seamlessly coexist. Communication is not suf- ficient, collaboration is necessary. Answering these hard questions requires a new breed of technology focused on Big Data, Analytics, AI, and human-machine-interac- tion. CFX brings us a step closer, but these developing technologies will close the smart factory loop. In summary, developing a smart factory strategy is the first step toward realizing oper- ational improvement. Leveraging existing lean manufacturing methodologies like value stream mapping is a sensible way to analyze your business and begin planning the evolu- tion. By focusing on incremental improvement, the complexity of the smart factory transfor- mation can be deconstructed into manageable phases with returns coming along the way. My white paper provides a starting point to under- stand the challenges facing the industry and the key smart factory concepts used to address to challenges. SMT007 A multidisciplinary team from the U.S. Department of Energy's Oak Ridge National Laboratory (ORNL) developed a computational framework to process and analyze large datasets of materials to determine how to improve the ion conduction process. Using a dataset containing over 80 different composi- tions of perovskites, the researchers identified and opti- mized those with promising proton conduction capabili- ties. These materials could enable the production of more reliable and efficient proton-conducting solid oxide fuel cells, for uses such as powering vehicles. Results from this work are published in The Journal of Physical Chemistry and Chemistry of Materials. These simulations revealed that correlations between lattice distortions and proton binding energy can make protons heavier and slower, inhibiting optimal proton con- duction. This revelation could help the researchers identify existing materials and develop new ones able to compete with acceptor dopant yttrium-doped BaZrO3, or Y-BZO. In addition to the practical benefits these results could have for energy applications, the team's newfound knowledge provides fundamental insights into scientific concepts. The researchers plan to expand their efforts beyond protons and perovskites to investigate the behav- ior of mobile ions in other categories of materials. Future findings could enhance the performance of other types of fuel cells as well as lithium-ion batteries. (Source: Oak Ridge National Laboratory) Simulations Identify Importance of Lattice Distortions in Ion-conducting Fuel Cell Materials

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