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Quantitative Evaluation of Irradiated Ductility Degradation Using the Indentation Technique Combined with Numerical Experiments.

作者信息

Wakui Takashi, Saito Shigeru, Futakawa Masatoshi

机构信息

J-PARC Center, Japan Atomic Energy Agency, Ibaraki 319-1195, Japan.

出版信息

Materials (Basel). 2024 Dec 3;17(23):5925. doi: 10.3390/ma17235925.

DOI:10.3390/ma17235925
PMID:39685361
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11643518/
Abstract

The ductile properties of irradiated materials are among of the important indicators related to their structural integrity. These properties are generally determined by performing tensile tests on irradiated materials in the irradiation environment. Indentation tests are used for evaluating ductile properties easily and rapidly. Constants in the Swift-type material constitutive equation were identified via inverse analysis using the Kalman filter, such that the numerical experimental results reproduced the indentation test results. Numerical tensile experiments were conducted using stress-strain curves with the identified constants to obtain nominal stress and strain curves. The identified yield stress, work hardening coefficient, and exponent were 200-1000 MPa, 1100-1500 Ma, and 0.5-0.7, respectively. Furthermore, two methods were proposed for evaluating the total elongation. Method I was used to calculate the total elongation based on the relationship between the total and uniform elongations obtained from the tensile tests performed on irradiated materials. Method II was used to determine the total elongation from the ductile failure criterion based on the relationship between the stress and strain states in the tensile specimen model using the numerical tensile experiment and failure strain evaluated from actual tensile experiments. Evaluated minimum total elongation was 10%. The evaluation results for ion-irradiated materials were similar to the tensile test results for irradiated materials.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20c3/11643518/f0502b80165f/materials-17-05925-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20c3/11643518/6b7eee2b275a/materials-17-05925-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20c3/11643518/f0502b80165f/materials-17-05925-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20c3/11643518/6b7eee2b275a/materials-17-05925-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20c3/11643518/f0502b80165f/materials-17-05925-g008.jpg

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本文引用的文献

1
Artificial Neural Networks for Predicting Plastic Anisotropy of Sheet Metals Based on Indentation Test.基于压痕试验的用于预测金属板材塑性各向异性的人工神经网络
Materials (Basel). 2022 Feb 24;15(5):1714. doi: 10.3390/ma15051714.
2
Extraction of mechanical properties of materials through deep learning from instrumented indentation.通过仪器压痕的深度学习提取材料的力学性能。
Proc Natl Acad Sci U S A. 2020 Mar 31;117(13):7052-7062. doi: 10.1073/pnas.1922210117. Epub 2020 Mar 16.