AI 中文总结
研究针对核结构材料小尺寸与标准尺寸试样夏比冲击性能相关性问题，提出基于机器学习的框架，通过特定映射和模型提取相关值，经验证其相关性优于传统方法，且推理无需全尺寸数据，适用于多种相关应用。
AI 中文摘要
可靠的小尺寸与全尺寸试样夏比冲击试验结果相关性对结构完整性评估至关重要，尤其在核应用中。虽有标准和分析方法，但准确性有限。本研究提出基于机器学习的框架，通过温度偏移和缩放残差投影映射吸收能量值，用双曲线正切模型提取相关值。用SA533B钢的389组匹配试验数据集验证，该方法相关性优于传统方法，R2值分别为0.942和0.892，且推理时无需全尺寸数据，适用于材料监测等应用。
英文摘要
Reliable correlations of Charpy impact test results between sub-sized and full-sized specimens are essential for structural integrity assessments, particularly in nuclear applications, where spatial constraints and limited material volume restrict specimen size. Although standards such as ASTM A370 and BS 7910 provide guidance on conversion methodologies, and numerous analytical correlation methods have been proposed in prior studies, these approaches generally have limited accuracy and their applicability is often constrained to specific materials, treatment conditions, and specimen geometries. In this study, a Machine Learning (ML)-based framework is proposed for correlating Charpy impact properties across specimen sizes. The proposed approach maps absorbed energy values across the full ductile-to-brittle transition region by applying a temperature shift combined with scaled residual projection, to align sub-sized test data with full-sized response. From the resulting temperature-energy profiles, the correlated values for upper shelf energy (USE) and ductile-to-brittle transition temperature (DBTT) are extracted by fitting data with a hyperbolic tangent model. The framework is validated using a dataset comprising 389 matched sub-sized and full-sized Charpy impact tests on SA533B steel. This ML-based approach demonstrates an improved correlation performance relative to conventional analytical methods, achieving R2 values of 0.942 for USE and 0.892 for DBTT. The trained ML models do not require access to full-sized Charpy data during inference, making this approach suitable for material surveillance programs, accelerated irradiation testing, and other applications involving small-size Charpy impact testing.
Comments27 pages, 8 figures
Journal refScientific Reports 16, 16202 (2026)
DOI：10.1038/s41598-026-47605-4
