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Enabling Energy Efficiency in Manufacturing Environments Through Deep Learning Approaches: Lessons Learned

Abstract : Currently, manufacturing industries are faced by ever-growing complexities. On the one hand, sustainability in economic and ecological domains should be considered in manufacturing. With respect to energy, many manufacturing companies still lack energy-efficient processes. On the other hand, Industry 4.0 provides large manufacturing datasets, which can potentially enhance energy efficiency. Here, traditional methods of data analytics reach their limits due to the increasing complexity, high dimensionality and variability in raw data of industrial processes. This paper outlines the potential of deep learning as an enabler for energy efficiency in manufacturing. We believe that enough consideration has not been given to make manufacturing efficient in terms of energy. In this paper, we present three manufacturing environments where available DL approaches are identified as opportunities for the realization of energy-efficient manufacturing.
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Submitted on : Thursday, January 30, 2020 - 10:12:28 AM
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M. Alvela Nieto, E. Nabati, D. Bode, M. Redecker, A. Decker, et al.. Enabling Energy Efficiency in Manufacturing Environments Through Deep Learning Approaches: Lessons Learned. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2019, Austin, TX, United States. pp.567-574, ⟨10.1007/978-3-030-29996-5_65⟩. ⟨hal-02460459⟩

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