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Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation

Qin, Jian, Liu, Ying ORCID:, Grosvenor, Roger ORCID:, Lacan, Franck ORCID: and Jiang, Zhigang 2019. Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production , p. 118702. 10.1016/j.jclepro.2019.118702

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The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. While existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0959-6526
Date of First Compliant Deposit: 10 October 2019
Date of Acceptance: 2 October 2019
Last Modified: 06 Nov 2023 17:26

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