Towards low-budget energy efficiency design in additive manufacturing based on variational scale-aware transformer

Wang, Kang; Xu, Jinghua; Zhang, Shuyou; Tan, Jianrong; Qin, Jing

doi:10.1016/j.jclepro.2023.136168

Cited by 12 publications

(2 citation statements)

References 30 publications

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“…The term energy consumption is found in quadrant I. This term is associated with research on energy saving or responsible energy consumption to reduce the consumption and waste of resources or materials necessary to produce energy [27]. Research has also been conducted to improve the energy efficiency of carbon fibre manufacturing through waste heat recovery [59].…”

Section: Discussionmentioning

confidence: 99%

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Machine-Learning Applications in Energy Efficiency: A Bibliometric Approach and Research Agenda

Valencia-Arias,

García-Pineda,

González-Ruiz

et al. 2023

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The high demand for energy resources due to the increasing number of electronic devices has prompted the constant search for different or alternative energy sources to reduce energy consumption, aiming to meet the high demand for energy without exceeding the consumption of natural sources. In this context, the objective of this study was to examine research trends in the machine-learning-based design of electrical and electronic devices. The methodological approach was based on the analysis of 152 academic documents on this topic selected from Scopus and Web of Science in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Quantity, quality, and structural indicators were calculated to contextualize its thematic evolution. The results showed a growing interest in the subject since 2019, mainly in the United States and China, which stand out as world powers in the information and communication technology industry. Moreover, most studies focused on developing devices for controlling, monitoring and reducing energy consumption, mainly in 5G and thermal comfort devices, primarily using deep-learning techniques.

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Section: Discussionmentioning

confidence: 99%

“…For this reason, recent research has focused on searching for ML models and bio-inspired algorithms to reduce energy consumption in IoT [26]. ML methods have also enabled device manufacturers to use these data to carefully calculate power consumption from the design stage [27].…”

Section: Introductionmentioning

confidence: 99%