A framework of energy consumption modelling for additive manufacturing using Internet of Things

Qin, Jian, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Grosvenor, Roger ORCID: https://orcid.org/0000-0001-8942-4640 2017. A framework of energy consumption modelling for additive manufacturing using Internet of Things. Procedia CIRP Conference on Manufacturing System 63 , pp. 307-312. 10.1016/j.procir.2017.02.036

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Abstract

The topic of ‘Industry 4.0’ has become increasingly popular in manufacturing and academia since it was first published. Under this trending topic, researchers and companies have pointed out many related capabilities required by current manufacturing systems, such as automation, interoperability, consciousness, and intelligence. Additive manufacturing (AM) is one of the most popular applications of Industry 4.0. Although AM systems tend to become increasingly automated and worry less, the issue of energy consumption still attracts attention, even in the Industry 4.0 era, and is related to many processing factors depending on different types of AM system. Therefore, defining the energy consumption behaviour and discovering more efficient usage methods in AM processes is established as being one of the most important research targets. In this paper, an Internet of Things (IoT) framework is designed for understanding and reducing the energy consumption of AM processes. A huge number and variety of real-time raw data are collected from the manufacturing system; this data is analysed by data analytical technologies, combining the material attributes parameter and design information. This data is uploaded to the cloud where more data will be integrated for discovering the energy consumption knowledge of AM systems. In addition, a case study is also presented in this paper, which a typical AM system is focused on the target system (EOS P700). The raw data is collected and analysed from this process. Then, based on the IoT framework, a novel energy consumption analysis proposal is proposed for this system specifically.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TS Manufactures
Publisher:	Elsevier
Date of First Compliant Deposit:	6 July 2017
Date of Acceptance:	20 February 2017
Last Modified:	24 Nov 2024 22:45
URI:	https://orca.cardiff.ac.uk/id/eprint/102080

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