Investigation of strain rate behavior of aluminium and AA2024 using crystal plasticity

Singh, Lakhwinder; Vohra, Sanjay; Sharma, Manu

doi:10.1016/j.matpr.2021.10.232

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…In this study, we focus on the effect of strain-rate on the stress-strain curve with various average grain sizes. The effect of strain-rate has been experimentally validated in Benzing et al [49], and some numerical studies using crystal plasticity finite element model have been done as well, such as those found in Singh et al [50]. The inputs of the GP are the average grain size, the strain-rate, the strain, i.e.…”

Section: Resultsmentioning

confidence: 97%

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Tran

Maupin

Rodgers

2022

Journal of Computing and Information Science in Engineering

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Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce or when the dimensionality is high, and monotonicity is supported by strong physical evidence.

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Section: Resultsmentioning

confidence: 97%

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Tran

Maupin

Rodgers

2022

Journal of Computing and Information Science in Engineering

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A Two‐Scale Model of Fretting Fatigue Crack Initiation Life Based on Long Short‐Term Memory Networks Improved by Genetic Algorithm

Xu,

Wu,

Yuan

et al. 2024

Fatigue Fract Eng Mat Struct

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Fretting fatigue is a common engineering failure phenomenon, often resulting in a shorter lifespan compared to plain fatigue. To consider the interaction between different scales, this study proposes a fully coupled two‐scale model based on continuum damage mechanics (CDM) and the crystal plastic finite element method (CPFEM) for the fretting fatigue crack initiation. Furthermore, the life data series are generated by employing feature engineering and long short‐term memory (LSTM) networks optimized with a genetic algorithm, ensuring the minimization of redundancy. Additionally, the genetic algorithm, enhanced by the Markov chain Monte Carlo method, was used to optimize the hyperparameters of the LSTM network. Simulation results indicate that the two‐scale model offers improved accuracy in predicting crack initiation life and provides physical information of crack initiation from different scales simultaneously.

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Accumulated crystal plasticity dissipation energy driven continuum damage two-scale model for fretting fatigue initiation life

Fang,

Yazhou

2024

International Journal of Damage Mechanics

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Fretting fatigue often occurs in the interfaces between components, subjected to complex multi-axial load states and high stress gradients at the contact edge region. For the prediction of fretting fatigue crack initiation and in-depth understanding of the crack initiation mechanism, it is essential to investigate the damage mechanisms across various scales and explore the underlying scale coupling mechanisms. By introducing a power-law based scale coupling relationship, a two-scale model of fretting fatigue crack initiation life is proposed by combining macroscopic continuum damage mechanics (CDM) with microscopic crystal plastic finite element method (CPFEM). The simulation results indicate that the predicted fretting fatigue initiation life shows better accuracy than the result predicted by single-scale CDM model. In case of low stress level the rate of accumulated dissipation energy can be clearly divided into two stages with turning points, whereas it exhibits a relatively uniform damage process under high stress level. Moreover, the proposed two-scale model partly provides physical explanation for fretting fatigue crack initiation based on the information from the microscale.

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Investigation of strain rate behavior of aluminium and AA2024 using crystal plasticity

Cited by 4 publications

References 17 publications

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

A Two‐Scale Model of Fretting Fatigue Crack Initiation Life Based on Long Short‐Term Memory Networks Improved by Genetic Algorithm

Accumulated crystal plasticity dissipation energy driven continuum damage two-scale model for fretting fatigue initiation life

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