Modeling of temperature-dependent ultimate tensile strength for metallic materials

He, Yan; Li, Weiguo; Yang, Mengqing; Ying, Li; Zhao, Zhongyong; Dong, Pan; Ma, Jianzuo; Zheng, Shifeng

doi:10.1016/j.jcsr.2022.107184

Cited by 14 publications

(3 citation statements)

References 57 publications

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“…Compared to traditional methods for predicting UTS, such as empirical model [66] and physical model, [67] the multimodal fusion learning method based on composition and microstructure images proposed in this work has wider applicability. The limitation of the empirical model is that it can only be applied to certain materials or under certain conditions, while for physical model, some model parameters are difficult to obtain, and the simplification and assumptions of the physical model limit the scope of the model.…”

Section: Resultsmentioning

confidence: 99%

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

Zhu,

Luo,

Chen

et al. 2024

Materials Genome Engineering Advances

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Exploring the “composition‐microstructure‐property” relationship is a long‐standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. Firstly, the composition and image information are collected from the literature and supplementary experiments, followed by the image segmentation and quantitative analysis of eutectic Si images. Subsequently, the quantitative analysis results are combined with other features for three‐step feature screening, and 12 key features are obtained. Finally, four machine‐learning models (i.e., decision tree, random forest, adaptive boosting, and extreme gradient boosting [XGBoost]) are used to predict the UTS of Al‐Si alloys. The results show that the quantitative analysis method proposed in this paper is superior to Image‐Pro Plus (IPP) software in some aspects. The XGBoost model has the best prediction performance with R2 = 0.94. Furthermore, five mixed features and their critical values that significantly affect UTS are identified. Our study provides enlightenment for the prediction of UTS of Al‐Si alloys from composition and microstructure, and would be applicable to other alloys.

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Section: Resultsmentioning

confidence: 99%

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

Zhu,

Luo,

Chen

et al. 2024

Materials Genome Engineering Advances

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“…Based on the proposed force-heat equivalence energy density principle, we have developed a series of novel theoretical characterization models of materials; for example, temperature-dependent models of yield strength, ultimate tensile strength, and fatigue strength for metallic materials. [28][29][30] Furthermore, the proposed force-heat equivalence energy density principle has been successfully extended to the theoretical characterization of the physical properties of materials, such as the temperature-dependent anti-phase boundary energy, vacancy formation energy, surface energy, grain boundary slip energy and dislocation climbing energy of the metallic materials, [31][32][33][34][35] and the temperature-dependent band gap, refractive index and Raman frequency of semiconductor materials. [36][37][38] These models offer a quantitative description of the properties affected by temperature due to the change in heat energy.…”

Section: The Justification Of the Force-heat Equivalence Energy Densi...mentioning

confidence: 99%

Theoretical characterization of the temperature-dependent saturation magnetization of magnetic metallic materials

Wu 吴,

Dong 董,

He 贺

et al. 2024

Chinese Phys. B

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In this study, based on the Force-Heat Equivalence Energy Density Principle, a theoretical model for magnetic metallic materials is developed which characterizes the temperature-dependent magnetic anisotropy energy by considering the equivalent relationship between magnetic anisotropy energy and heat energy, and then the relationship between the magnetic anisotropy constant and saturation magnetization is considered. Finally, we formulate a temperature-dependent model for saturation magnetization, revealing the inherent relationship between temperature and saturation magnetization. Our model predicts the saturation magnetization for nine different magnetic metallic materials at different temperatures, exhibiting satisfactory agreement with experimental data. Additionally, the experimental data used as reference points are at or near room temperature. Compared to other phenomenological theoretical models, this model is considerably more accessible than the data required at 0 K. The index included in our model is set to a constant value which is equal to 10/3 for materials other than Fe, Co, and Ni. For transition metals (Fe, Co, and Ni in this paper), the index is 6 in the range of 0 K to 0.65T cr (critical temperature), and 3 in the range of 0.65T cr to T cr, unlike other models where the adjustable parameters vary according to each material. In addition, our model provides a new way for designing and evaluating magnetic metallic materials with superior magnetic properties over a wide range of temperatures.

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“…En la actualidad, el material estructural más utilizado en el campo de la ingeniería, esto se debe a que los materiales metálicos se utilizan en diversas aplicaciones, que van desde la construcción de edificios de gran altura, como soporte estructural de centrales eléctricas, solventar reactores nucleares, construcción de motores de turbinas de gas y otros campos como la construcción de máquinas herramientas en donde se ha convertido en el material fundamental de este tipo de aplicación (He et al, 2022).…”

Section: Materiales Metálicos En La Actualidadunclassified

Materiales metálicos: Revisión del estado del arte

Verdezoto Espinoza,

Rosero Ruano,

Balla Yucailla

et al. 2023

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Introducción: Los materiales metálicos han utilizado durante muchos años, en una diversa gama de aplicaciones a nivel industrial, esto debido a que poseen excelentes propiedades físicas y mecánicas. Los materiales metálicos son elementos producidos mediante varios cambios de manufactura, para desarrollar aleaciones con mejores propiedades, como mayor resistencia y tenacidad. En la industria, es importante conocer los procesos que se encuentran en disposición para la selección de una mejor opción y optimización en el consumo del material, así obteniendo un aumento en la eficiencia de la producción. Metodología: Este artículo indaga los avances de los materiales metálicos y su relevancia en la industria actual, analizando sus características químicas y mecánicas, y su influencia en diversas aplicaciones en sus aleaciones con compuestos de Hierro-Carbono, a bajas conductividad con distintos elementos en su composición. Resultados: Se resalta la importancia de mantenerse informado en este campo para utilizar al máximo las ventajas que ofrecen estos materiales en un entorno industrial competitivo. Es fundamental chequear investigaciones previas y estudios científicos para la mejor comprensión de los materiales metálicos, así como presentar ejemplos de aplicaciones industriales para resaltar sus beneficios y desafíos. El entendimiento de los procesos de manufactura y la elección adecuada de materiales son elementos importantes para mejorar la eficiencia y reducir el desecho de recursos en la producción industrial. Conclusión: los materiales metálicos continúan siendo esenciales para la industria debido a sus propiedades avanzadas y su capacidad para satisfacer diversas necesidades de aplicación. Área de estudio general: ciencia de los materiales, Área de estudio específica: resistencia de los materiales, Tipo de estudio: revisión bibliográfica.

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Modeling of temperature-dependent ultimate tensile strength for metallic materials

Cited by 14 publications

References 57 publications

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning

Theoretical characterization of the temperature-dependent saturation magnetization of magnetic metallic materials

Materiales metálicos: Revisión del estado del arte

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