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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.

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Reduction of energy consumption in the steel industry is a global issue where government is actively taking measures to pursue. A steel plant can manage its energy better if the consumption can be modelled and predicted. The existing methods used for energy consumption modelling rely on the quantity of labelled data. However, if the labelled energy consumption data is deficient, its underlying process of modelling and prediction tends to be difficult. The purpose of this study is to establish an energy value prediction model through a big data-driven approach. Owing to the fact that labelled energy data is often limited and expensive to obtain, while unlabelled data is abundant in the realworld industry, a semi-supervised learning approach, i.e., deep learning embedded semi-supervised learning (DLeSSL), is proposed to tackle the issue. Based on DLeSSL, unlabelled data can be labelled and compensated using a semi-supervised learning approach that has a deep learning technique embedded so to expand the labelled data set. An experimental study using a large amount of furnace energy consumption data shows the merits of the proposed approach. Results derived using the proposed method reveal that deep learning (DLeSSL based) outperforms the deep learning (supervised) and deep learning (label propagation based) when the labelled data is limited. In addition, the effect on performance due to the size of labelled data and unlabelled data is also reported.