Multi-source data analytics for AM energy consumption prediction. (October 2018)

• Record Type: Journal Article
• Authors: Qin, Jian; Liu, Ying; Grosvenor, Roger
• Is Part Of: Advanced engineering informatics. Volume 38(2018)
• Page Start: 840
• Page End: 850
• Publication Date: 2018-10
• Subjects: Additive manufacturing -- Energy consumption prediction -- Clustering -- Deep learning -- Internet of things; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.10.008
• Languages: English
• ISSNs: 1474-0346

Abstract

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.