4.6 Article

Plastic anisotropy of AA7075-T6 alloy under quasi-static compression: experiments, classical plasticity and artificial neural networks modeling

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Transportation Science & Technology

Coupling effects of strain rate and fatigue damage on wheel-rail contact behaviour: a dynamic finite element simulation

Xiongfei Zhou et al.

Summary: In this study, a comprehensive 3-D explicit finite element model was established to investigate the frictional wheel-rail rolling contact responses. The strain rate effect and initial fatigue damage of wheel/rail materials were considered, and four friction exploitation levels were included to examine the dynamic contact responses.

INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION (2023)

Article Engineering, Mechanical

Inner blast response of fiber reinforced aluminum tubes

Xin Li et al.

Summary: In this study, the dynamic failure of Fiber Reinforced Metal Tubes (FRMTs) under inner blast load was investigated experimentally. FRMTs were prepared by winding basalt fiber or H-glass fiber onto an aluminum lining through filament winding process. The effects of explosive mass, winding angle, and number of layers on the failure modes were discussed. The results showed that FRMTs had better anti-blast performance compared to metallic tubes, and the best performance was achieved with a winding angle of +/- 55 degrees.

INTERNATIONAL JOURNAL OF IMPACT ENGINEERING (2023)

Article Mechanics

Strain-rate dependent tensile behavior of railway wheel/rail steels with equivalent fatigue damage: Experiment and constitutive modeling

Lin Jing et al.

Summary: This study investigates the dynamic constitutive relationship of railway wheel/rail steels and develops a modified JC model to describe the plastic flow behavior of the materials with initial fatigue damage.

ENGINEERING FRACTURE MECHANICS (2022)

Article Engineering, Multidisciplinary

Parametric deep energy approach for elasticity accounting for strain gradient effects

Vien Minh Nguyen-Thanh et al.

Summary: The Parametric Deep Energy Method (P-DEM) presented in this work solves elasticity problems considering strain gradient effects using physics-informed neural networks (PINNs). It does not require classical discretization and simplifies implementation by defining potential energy. Normalized inputs in a parametric/reference space prevent vanishing gradients and enable faster convergence. The method utilizes NURBS basis functions for forward-backward mapping and Gauss quadrature for approximating total potential energy in the loss function.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Engineering, Mechanical

Deep learning for plasticity and thermo-viscoplasticity

Diab W. Abueidda et al.

Summary: This study applied sequence learning models to predict the history-dependent responses of materials, showing that gated recurrent unit and temporal convolutional network can accurately learn and instantly predict such phenomena, with TCN being more computationally efficient during the training process.

INTERNATIONAL JOURNAL OF PLASTICITY (2021)

Article Materials Science, Multidisciplinary

Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation

Jici Wen et al.

Summary: The research group developed a physics-driven machine learning algorithm to predict the deformation of Li-metal, achieving a high-fidelity deformation map. The integration of finite element procedure with PD-ML allows for a more accurate description of the mechanical response of Li-metal.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2021)

Article Engineering, Multidisciplinary

Optimizing the neural network hyperparameters utilizing genetic algorithm

Saeid Nikbakht et al.

Summary: This study applies genetic algorithm to optimize the hyperparameters of neural networks, utilized deep energy method on Timoshenko beam and plate with a hole, and achieved high accuracy in predicting stress distribution through optimization of hidden layers, integration points, and neurons in each layer.

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A (2021)

Article Materials Science, Multidisciplinary

A machine learning approach to fracture mechanics problems

Xing Liu et al.

ACTA MATERIALIA (2020)

Article Engineering, Multidisciplinary

An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

E. Samaniego et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

Article Materials Science, Multidisciplinary

Experimental Investigation and Constitutive Description of Railway Wheel/Rail Steels under Medium-Strain-Rate Tensile Loading

Xingya Su et al.

JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE (2020)

Article Engineering, Mechanical

Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

Somdatta Goswami et al.

THEORETICAL AND APPLIED FRACTURE MECHANICS (2020)

Article Materials Science, Multidisciplinary

On the potential of recurrent neural networks for modeling path dependent plasticity

Maysam B. Gorji et al.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2020)

Article Engineering, Mechanical

Neural network model describing the temperature- and rate-dependent stress-strain response of polypropylene

Benoit Jordan et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2020)

Article Engineering, Mechanical

Application of artificial neural networks in micromechanics for polycrystalline metals

Usman Ali et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2019)

Article Materials Science, Multidisciplinary

Predicting the mechanical response of oligocrystals with deep learning

A. L. Frankel et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

Article Multidisciplinary Sciences

Deep learning predicts path-dependent plasticity

M. Mozaffar et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Proceedings Paper Engineering, Mechanical

Towards neural network models for describing the large deformation behavior of sheet metal

Maysam B. Gorji et al.

38TH INTERNATIONAL DEEP DRAWING RESEARCH GROUP ANNUAL CONFERENCE (IDDRG 2019) (2019)

Article Computer Science, Interdisciplinary Applications

A comparative study of machine learning approaches for modeling concrete failure surfaces

Uwe Reuter et al.

ADVANCES IN ENGINEERING SOFTWARE (2018)

Article Mechanics

Effect of the kinematic hardening on the plastic anisotropy parameters for metallic sheets

Houssem Badreddine et al.

COMPTES RENDUS MECANIQUE (2018)

Article Materials Science, Multidisciplinary

Compressive strain rate dependence and constitutive modeling of closed-cell aluminum foams with various relative densities

Lin Jing et al.

JOURNAL OF MATERIALS SCIENCE (2018)

Article Mechanics

Plastic anisotropy and ductile fracture of bake-hardened AA6013 aluminum sheet

Jinjin Ha et al.

INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES (2018)

Article Engineering, Mechanical

A normalized stress invariant-based yield criterion: Modeling and validation

Qi Hu et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2017)

Article Engineering, Multidisciplinary

A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

M. A. Bessa et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2017)

Article Engineering, Mechanical

Anisotropic and asymmetrical yielding and its distorted evolution: Modeling and applications

H. Li et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2016)

Article Materials Science, Multidisciplinary

The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets

Arash Jenab et al.

MATERIALS & DESIGN (2016)

Article Materials Science, Multidisciplinary

An evolutionary anisotropic model for sheet metals based on non-associated flow rule approach

Mohsen Safaei et al.

COMPUTATIONAL MATERIALS SCIENCE (2014)

Article Engineering, Mechanical

Asymmetric yield function based on the stress invariants for pressure sensitive metals

Jeong Whan Yoon et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2014)

Article Engineering, Mechanical

Consideration of strength differential effect in sheet metals with symmetric yield functions

Yanshan Lou et al.

INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES (2013)

Article Engineering, Mechanical

Anisotropic failure modes of high-strength aluminium alloy under various stress states

M. Fourmeau et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2013)

Article Materials Science, Multidisciplinary

The equivalent plastic strain-dependent Yld2000-2d yield function and the experimental verification

Haibo Wang et al.

COMPUTATIONAL MATERIALS SCIENCE (2009)

Article Engineering, Mechanical

Anisotropic hardening and non-associated flow in proportional loading of sheet metals

Thomas B. Stoughton et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2009)

Article Engineering, Mechanical

On linear transformations of stress tensors for the description of plastic anisotropy

Frederic Barlat et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2007)

Article Engineering, Mechanical

Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network

MS Al-Haik et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2006)

Article Engineering, Mechanical

A criterion for description of anisotropy and yield differential effects in pressure-insensitive metals

O Cazacu et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2004)

Article Engineering, Multidisciplinary

Application of the theory of representation to describe yielding of anisotropic aluminum alloys

O Cazacu et al.

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE (2003)

Article Materials Science, Multidisciplinary

Generalization of Drucker's yield criterion to orthotropy

O Cazacu et al.

MATHEMATICS AND MECHANICS OF SOLIDS (2001)