{"title":"Decoding ceramic fracture: Atomic defects studies in multiscale simulations","authors":"","doi":"10.1016/j.ijmecsci.2024.109647","DOIUrl":null,"url":null,"abstract":"<div><p>Microstructural atomic defects, including voids, cleavage, and inclusions, are commonly observed in alumina materials, and their impact on mechanical properties, such as fracture stress and toughness, is significant. In this paper, we introduce novel alumina models that incorporate experimentally observed void features. An atomic model is established to study the effects of micro-structural void features on fracture properties and atomic structure changes using molecular dynamics simulations. The electron backscatter diffraction and scanning electronic microscopy analysis of experimental samples are used to evaluate microstructural features that are used as inputs to the simulations (e.g., void aspect ratio, void angle). We apply an innovative Atomistic-to-Continuum (ATC) method based on Riemann sums to bridge atomic and continuum mechanics theories, evaluating the resistance of materials with atomic defects to crack propagation. The results show the greatest effects of pore angles on weakening mechanical properties such as peak strength and fracture energy density. The accuracy and efficiency of the ATC method in evaluating stress intensity factors are used to calculate the mechanical responses. Additionally, we establish a multiple layer perceptron neural network to evaluate the complex relationship between void features (aspect ratio, pore angle, relative distance) and typical fracture properties (fracture stress, critical stress intensity factor). A meta-analysis of these results from both machine learning methods and molecular dynamics simulations reveals the significant impact of each void feature on the sensitivity of typical fracture properties (peak strength, critical stress intensity factor at peak strength) and highlights the critical role of aspect ratio on fracture properties.</p></div>","PeriodicalId":56287,"journal":{"name":"International Journal of Mechanical Sciences","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Mechanical Sciences","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S002074032400688X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Microstructural atomic defects, including voids, cleavage, and inclusions, are commonly observed in alumina materials, and their impact on mechanical properties, such as fracture stress and toughness, is significant. In this paper, we introduce novel alumina models that incorporate experimentally observed void features. An atomic model is established to study the effects of micro-structural void features on fracture properties and atomic structure changes using molecular dynamics simulations. The electron backscatter diffraction and scanning electronic microscopy analysis of experimental samples are used to evaluate microstructural features that are used as inputs to the simulations (e.g., void aspect ratio, void angle). We apply an innovative Atomistic-to-Continuum (ATC) method based on Riemann sums to bridge atomic and continuum mechanics theories, evaluating the resistance of materials with atomic defects to crack propagation. The results show the greatest effects of pore angles on weakening mechanical properties such as peak strength and fracture energy density. The accuracy and efficiency of the ATC method in evaluating stress intensity factors are used to calculate the mechanical responses. Additionally, we establish a multiple layer perceptron neural network to evaluate the complex relationship between void features (aspect ratio, pore angle, relative distance) and typical fracture properties (fracture stress, critical stress intensity factor). A meta-analysis of these results from both machine learning methods and molecular dynamics simulations reveals the significant impact of each void feature on the sensitivity of typical fracture properties (peak strength, critical stress intensity factor at peak strength) and highlights the critical role of aspect ratio on fracture properties.
期刊介绍:
The International Journal of Mechanical Sciences (IJMS) serves as a global platform for the publication and dissemination of original research that contributes to a deeper scientific understanding of the fundamental disciplines within mechanical, civil, and material engineering.
The primary focus of IJMS is to showcase innovative and ground-breaking work that utilizes analytical and computational modeling techniques, such as Finite Element Method (FEM), Boundary Element Method (BEM), and mesh-free methods, among others. These modeling methods are applied to diverse fields including rigid-body mechanics (e.g., dynamics, vibration, stability), structural mechanics, metal forming, advanced materials (e.g., metals, composites, cellular, smart) behavior and applications, impact mechanics, strain localization, and other nonlinear effects (e.g., large deflections, plasticity, fracture).
Additionally, IJMS covers the realms of fluid mechanics (both external and internal flows), tribology, thermodynamics, and materials processing. These subjects collectively form the core of the journal's content.
In summary, IJMS provides a prestigious platform for researchers to present their original contributions, shedding light on analytical and computational modeling methods in various areas of mechanical engineering, as well as exploring the behavior and application of advanced materials, fluid mechanics, thermodynamics, and materials processing.