Scholarlink

<p>The integration of artificial intelligence (AI) with digital twin (DT) technology has revolutionised the industry by enabling the creation of autonomous, adaptive, and resilient systems that are beyond static digital replicas. AI-enhanced DTs facilitate real-time monitoring, predictive maintenance, proactive decision making, and operational efficiency, aligning with the human-centric objectives of Industry 5.0. In this study, an AI–DT Integration framework is introduced, AI is systematically incorporated into the DT lifecycle across virtualisation and synchronisation, monitoring and awareness, and decision-making and optimisation phases. By employing advanced techniques, such as generative design, predictive analytics, and scenario simulations, this framework enhances DT autonomy and resilience while addressing critical challenges such as interoperability, scalability, and data security. Case studies have demonstrated the transformative impact of AI on DT functionality, including self-optimisation, adaptive scheduling, and risk mitigation. These findings underscore the potential of AI-driven DTs to revolutionise industries and urban systems, highlighting the need for global standards and scalable architectures to realise their role as foundational tools in sustainable and adaptive Industry 5.0 ecosystems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月3日

查看全文
  • Seyed Mohammad Mehdi Sajadieh,
  • Sang Do Noh

摘要

The integration of artificial intelligence (AI) with digital twin (DT) technology has revolutionised the industry by enabling the creation of autonomous, adaptive, and resilient systems that are beyond static digital replicas. AI-enhanced DTs facilitate real-time monitoring, predictive maintenance, proactive decision making, and operational efficiency, aligning with the human-centric objectives of Industry 5.0. In this study, an AI–DT Integration framework is introduced, AI is systematically incorporated into the DT lifecycle across virtualisation and synchronisation, monitoring and awareness, and decision-making and optimisation phases. By employing advanced techniques, such as generative design, predictive analytics, and scenario simulations, this framework enhances DT autonomy and resilience while addressing critical challenges such as interoperability, scalability, and data security. Case studies have demonstrated the transformative impact of AI on DT functionality, including self-optimisation, adaptive scheduling, and risk mitigation. These findings underscore the potential of AI-driven DTs to revolutionise industries and urban systems, highlighting the need for global standards and scalable architectures to realise their role as foundational tools in sustainable and adaptive Industry 5.0 ecosystems.