YU Xiaohang, ZHOU Zhou, HAO Huijie, ZHAO Jingyi, QI Kaili, ZHANG Linan, LU Zhongliang
Global nuclear energy technology is currently entering a new phase of rapid development. As the primary line of defense in ensuring nuclear safety, nuclear radiation shielding materials face an urgent demand for the simultaneous optimization of multiple objectives, including high shielding performance, lightweight design, radiation resistance, and cost control. However, traditional research and development methods have long relied on trial-and-error approaches and simulations based on single physical mechanisms, leading to prolonged development cycles, low efficiency, and difficulties in multi-objective coordination. Therefore, new research paradigms are urgently needed. The work aims to review the research progress on nuclear radiation shielding materials involving emerging technologies such as artificial intelligence (AI), digital twins (DT), and additive manufacturing (AM), analyze the key challenges hindering their deep integration, and provide guidance for the implementation of intelligent design and manufacturing in this field. Through a literature review, recent research findings and technological advancements in AI, DT, and AM in the composition design, structural optimization, manufacturing processes, quality control, and in-service monitoring of nuclear radiation shielding materials were systematically summarized. On this basis, the core challenges faced in the deep integration and engineering application of these technologies were further analyzed, primarily including the small-sample problem caused by limited access to high-quality training data, insufficient generalization capabilities of AI models across different materials and process conditions, high costs associated with digital twin modeling and difficulties in real-time synchronization, as well as key issues such as the poor adaptability of additive manufacturing processes and the lack of nuclear-grade certification standards. The deep integration of emerging technologies such as AI, DT, and AM has demonstrated tremendous potential in the field of nuclear radiation shielding materials and has achieved certain successes in specific application scenarios. However, to bridge the gap between laboratory research and large-scale engineering applications, breakthroughs are still needed in key areas such as establishing data-sharing mechanisms, enhancing model generalization capabilities, developing efficient computational methods and radiation-resistant sensing technologies, and optimizing additive manufacturing processes and standard certification. This work systematically reviews intelligent technology methods and their applications across the key stages of the entire R&D chain for nuclear radiation shielding materials, covering aspects from composition design and structural optimization to manufacturing process control, quality inspection, and in-service monitoring, providing methodological guidance and practical references for the efficient design and reliable manufacturing of nuclear radiation shielding materials.
