Browsing by Author "Assad, Fadi"

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    A framework for enabling metaverse for sustainable manufacturing
    (Elsevier, 2024) Assad, Fadi; Konstantinov, Sergey; Patsavellas, John; Salonitis, Konstantinos
    Newly introduced technologies often require time for adoption and integration into manufacturing environments, for several reasons including technological maturity, adoption costs, and skills gaps. The inclusion of sustainability as a new requirement for both customers and producers adds further complexity to the equation. As metaverse technology became available, it became logical to establish a set of requirements to harness its new potential and create a sustainability-oriented framework for seamless integration into modern smart manufacturing environments. Against this background, the current work introduces a framework aimed at harnessing the potential of the metaverse to enhance manufacturing sustainability. As a case study, an industrial workshop was analysed and evaluated using the proposed framework. The findings help create a future plan for leveraging the use of the metaverse and prioritising its requirements.
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    ItemOpen Access
    Enhancing sustainability in manufacturing through cognitive digital twins powered by generative artificial intelligence
    (Elsevier, 2024-11) Assad, Fadi; Patsavellas, John; Salonitis, Konstantinos
    The rise of Industry 4.0 has brought new advancements in manufacturing, with a focus on integrating digital technologies to optimise processes and increase sustainability. Cognitive Digital Twins (CDTs) are emerging as a powerful paradigm in this area. They leverage advanced analytics, artificial intelligence (AI), and machine learning to create dynamic, real-time representations of physical manufacturing systems. This paper explores how CDTs can improve sustainability within the manufacturing sector. It proposes integrating generative artificial intelligence (GenAI) into the platforms that operate these digital twins to grant them cognitive capabilities. The work introduces a method for mapping and integrating energy consumption data to an Internet of Things (IoT) platform that includes the digital twin and a generative AI language model, such as ChatGPT. This proposed approach serves as a stepping stone towards unlocking the full potential of CDTs. It empowers manufacturers to achieve higher levels of sustainability and environmental responsibility.