The yield strength of metallic materials is an important mechanical property in materials science and engineering. Understanding and predicting yield strength has always been a key focus for scientists. The Hall-Petch relationship is commonly used to describe the relationship between yield strength and grain size in polycrystalline metallic materials. However, this relationship deviates when the grain size is larger (above millimeter level) or smaller (nanometer level).
Graphical Abstract
Methods
Researchers from the University of Science and Technology Beijing utilized a data-driven machine learning method to explore the intrinsic factors of the fitting constants and in the traditional Hall-Petch relationship. A new Hall-Petch model was constructed through symbolic regression methods.
Highlights
Revealed the physical essence of the constants in the Hall-Petch relationship: identified five key physical quantities affecting, namely valence electron distance, cohesive energy, coefficient of linear thermal expansion, grain boundary interface energy, and Young's modulus, revealing the physical essence of the constants in the Hall-Petch relationship. Constructed a new Hall-Petch model: Based on a data-driven machine learning method, a new Hall-Petch model was established. This model can directly predict the yield strength of polycrystalline metals through the calculation of key physical quantities, without the need for experimental fitting.
The model has excellent generalization ability: The new Hall-Petch model is not only applicable to pure metals with grain sizes in the range of 1∼1000 μm, but can also be extended to the yield strength prediction of nanocrystalline metals and single-phase alloys, demonstrating excellent generalization ability.
Provides a theoretical method for trans-scale calculation of metallic materials: The new Hall-Petch model can be extended to the correlation calculation between the composition, grain structure, and mechanical properties of single-phase alloys, providing a theoretical method for the trans-scale calculation of metallic materials.
Provides new ideas for material design: By revealing the physical mechanism of the Hall-Petch relationship and providing a new theoretical model for predicting yield strength, this study provides new ideas for material design and contributes to the design of metallic materials with higher yield strength.
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