🤖 AI Summary
This study addresses the challenge of accurately correlating impact toughness between miniature and standard Charpy specimens in nuclear structural material assessment, where spatial and material constraints often necessitate the use of small-scale samples yet lack a high-precision, universally applicable mapping method. The authors propose a machine learning–based cross-specimen-size framework that aligns the impact energy curves of miniature specimens to the standard response through temperature shifting and scaled residual projection, followed by hyperbolic tangent model fitting to extract the upper shelf energy (USE) and ductile-to-brittle transition temperature (DBTT). Notably, this approach enables full-transition-region correlation without requiring any reference data from standard specimens, making it suitable for material surveillance and accelerated irradiation testing. Validated on 389 datasets of SA533B steel, the method achieves R² values of 0.942 and 0.892 for USE and DBTT predictions, respectively, substantially outperforming conventional analytical techniques.
📝 Abstract
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.