Materials informatics for mechanical deformation: A review of applications and challenges
K Frydrych, K Karimi, M Pecelerowicz, R Alvarez… - Materials, 2021 - mdpi.com
In the design and development of novel materials that have excellent mechanical properties,
classification and regression methods have been diversely used across mechanical …
classification and regression methods have been diversely used across mechanical …
Prediction of compressive mechanical properties of three-dimensional mesoscopic aluminium foam based on deep learning method
W Zhuang, E Wang, H Zhang - Mechanics of Materials, 2023 - Elsevier
To achieve efficient and accurate prediction of the mechanical properties of aluminium foam,
this study proposes a deep learning-based mechanical property prediction framework. The …
this study proposes a deep learning-based mechanical property prediction framework. The …
Prediction of creep failure time using machine learning
A subcritical load on a disordered material can induce creep damage. The creep rate in this
case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady …
case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady …
Deep learning method for predicting the mechanical properties of aluminum alloys with small data sets
Z Yu, S Ye, Y Sun, H Zhao, XQ Feng - Materials Today Communications, 2021 - Elsevier
Big data is usually needed for a deep learning method to predict the properties of materials,
but, in practice, only limited data sets are available for engineering materials. In this study …
but, in practice, only limited data sets are available for engineering materials. In this study …
Detection of the onset of yielding and creep failure from digital image correlation
There are a multitude of applications in which structural materials would be desired to be
nondestructively evaluated, while in a component, for plasticity and failure characteristics. In …
nondestructively evaluated, while in a component, for plasticity and failure characteristics. In …
[HTML][HTML] Predicting creep failure by machine learning-which features matter?
Spatial and temporal features are studied with respect to their predictive value for failure
time prediction in subcritical failure with machine learning (ML). Data are generated from …
time prediction in subcritical failure with machine learning (ML). Data are generated from …
Prediction of depinning transitions in interface models using Gini and Kolkata indices
The intermittent dynamics of driven interfaces through disordered media and its subsequent
depinning for large enough driving force is a common feature for a myriad of diverse …
depinning for large enough driving force is a common feature for a myriad of diverse …
Reduced-order models for ranking damage initiation in dual-phase composites using Bayesian neural networks
A Venkatraman, D Montes de Oca Zapiain… - JOM, 2020 - Springer
The design and development of materials with increased damage resilience is often
impeded by the difficulty in establishing the precise linkages, with quantified uncertainty …
impeded by the difficulty in establishing the precise linkages, with quantified uncertainty …
Direct detection of plasticity onset through total-strain profile evolution
S Papanikolaou, MJ Alava - Physical Review Materials, 2021 - APS
Plasticity in solids is dependent on microstructural history, temperature, and loading rate,
and sample-dependent knowledge of yield points in structural materials adds reliability to …
and sample-dependent knowledge of yield points in structural materials adds reliability to …
Computational Mechanics with Deep Learning
G Yagawa, A Oishi - Computational Mechanics with Deep Learning: An …, 2022 - Springer
The present chapter overviews recent research trends of deep learning related to
computational mechanics. In Sect. 3.1, we see the growing interest in deep learning in …
computational mechanics. In Sect. 3.1, we see the growing interest in deep learning in …