Multi-source data analytics for AM energy consumption prediction |
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Affiliation: | 1. Department of Construction and Real Estate, Southeast University, Nanjing 210096, PR China;2. Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing 210096, PR China;3. Dept. of Building Construction, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA;4. Dept. of Civil and Environmental Engineering, University of Maryland, College Park, USA;5. Institute for Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland;6. Chaoyang University of Technology, Taichung, Taiwan;1. College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China;2. Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing, 100029, China |
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Abstract: | The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches. |
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Keywords: | Additive manufacturing Energy consumption prediction Clustering Deep learning Internet of things |
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