Coupled continuum damage mechanics and crystal plasticity model and its application in damage evolution in polycrystalline aggregates

Amelirad, Omid; Assempour, Ahmad

doi:10.1007/s00366-021-01346-2

Cited by 23 publications

(6 citation statements)

References 69 publications

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“…The microstructure of the austenitic steels could be revealed using several etchants. One of the best etchants are 60% nitric acid in distilled water at 1 V direct current for 120 s 38 . However, this etchant only reveals grain boundaries and the twin boundaries could not be recognized as could be observed in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The forming limit evaluations follow a routine procedure using hemisphere punch test 37 , 38 , 45 , 66 . In total, six samples with the dimensions given in Fig.…”

Section: Cold Rolling Processmentioning

confidence: 99%

“…Their simulation reveals that increase in grain size shifts forming limit curves upward in FLD. Moreover, grain orientation and morphology effects on the void nucleation were examined by the same authors 38 .…”

Section: Introductionmentioning

confidence: 99%

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ANFIS-based forming limit prediction of stainless steel 316 sheet metals

Zhang¹,

Meng

Shariati

2023

Sci Rep

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Effect of microstructure on the formability of the stainless sheet metals is a major concern for engineers in sheet industries. In the case of austenitic steels, existence of strain-induced martensite ($${\alpha }^{^{\prime}}$$ α ′ -martensite) in their micro structure causes considerable hardening and formability reduction. In the present study, we aim to evaluate the formability of AISI 316 steels with different intensities of martensite via experimental and artificial intelligence methods. In the first step, AISI 316 grade steels with 2 mm initial thicknesses are annealed and cold rolled to various thicknesses. Subsequently, the relative area of strain-induced martensite are measured using metallography tests. Formability of the rolled sheets are determined using hemisphere punch test to obtain forming limit diagrams (FLDs). The data obtained from experiments were further utilized to train and validate an artificial neural fuzzy interfere system (ANFIS). After training the ANFIS, predicted major strains by the neural network are compared to a new set experimental results. The results indicate that cold rolling has unfavorable effects on the formability of this type of stainless steels while significantly strengthens the sheets. Moreover, the ANFIS exhibits satisfactory results in comparison to the experimental measurements.

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Section: Methodsmentioning

confidence: 99%

“…The forming limit evaluations follow a routine procedure using hemisphere punch test 37 , 38 , 45 , 66 . In total, six samples with the dimensions given in Fig.…”

Section: Cold Rolling Processmentioning

confidence: 99%

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ANFIS-based forming limit prediction of stainless steel 316 sheet metals

Zhang¹,

Meng

Shariati

2023

Sci Rep

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“…Compared to experiments, simulations allow polycrystalline aggregates to achieve significantly higher finite strains at lower strain rates, and can be used to explore a wide range of deformation conditions by varying parameters such as the sample size, strain rate, and temperature. Moreover, numerical simulations allow studying the influence of deformation mechanisms in isolation and also how they interact when they are coupled (Amelirad & Assempour, 2022; Bons et al., 2008; Eisenlohr et al., 2013; Lebensohn, 2001; Otsuka et al., 2018; Pokharel et al., 2014). Several numerical approaches have been developed to simulate dynamic recrystallization of polycrystalline materials.…”

Section: Introductionmentioning

confidence: 99%

Full‐Field Numerical Simulation of Halite Dynamic Recrystallization From Subgrain Rotation to Grain Boundary Migration

Hao,

Llorens,

Griera

et al. 2023

JGR Solid Earth

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Full‐field numerical modeling is a useful method to gain understanding of rock salt deformation at multiple scales, but it is quite challenging due to the anisotropic and complex plastic behavior of halite, together with dynamic recrystallization processes. This contribution presents novel results of full‐field numerical simulations of coupled dislocation glide and dynamic recrystallization of halite polycrystalline aggregates during simple shear deformation, including both subgrain rotation and grain boundary migration (GBM) recrystallization. The results demonstrate that the numerical approach successfully replicates the evolution of pure halite microstructures from laboratory torsion deformation experiments at 100–300°C. Temperature determines the competition between (a) grain size reduction controlled by dislocation glide and subgrain rotation recrystallization (at low temperature) and (b) grain growth associated with GBM (at higher temperature), while the resulting crystallographic preferred orientations are similar for all cases. The relationship between subgrain misorientation and strain follows a power law relationship with a universal exponent of 2/3 at low strain. However, dynamic recrystallization causes a progressive deviation from this relationship when strain increases, as revealed by the skewness of the subgrain misorientation distribution. A systematic investigation of the subgrain misorientation evolution shows that strain or temperature prediction from microstructures requires careful calibration.

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“…Numerical solutions in the structural and solid mechanics are widespread and give acceptable and accurate results specifically in nonlinear problems (Amelirad and Assempour, 2019, 2021; Massoumi et al, 2018). However, the time of computation in such solution approaches is a challenge in utilizing these methods in online approaches.…”

Section: Introductionmentioning

confidence: 99%

On the wave dispersion of a multi-scale hybrid nanocomposite microstructure via deep neural network technique

Wei

2023

Journal of Vibration and Control

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The utilization of machine learning advantages in solving engineering problems has proven to be beneficial and accurate. Dealing with partial differential equations (PDEs), on the other hand, PINN could be enhanced using additional information from the physical mathematics of the problems. In this regard, a physics-informed neural network (PINN) is exploited in the current study to extract phase and group velocities of the multi-scale hybrid composite plate structure. In doing so, the exact differential equations of homogenized plate structure are extracted using Hamilton’s principle based on modified couple stress theory and shear deformation displacement field. The boundary conditions along with initial conditions are also considered in the equations. The PINN tries to minimize the loss function which is defined using extracted PDEs, boundary conditions, and initial conditions. Moreover, the results of PINN are further compared with the analytical solution using harmonic expansion of the displacement variables. The results show highly accurate and reliable results obtained using PINN in comparison to analytical results.

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Coupled continuum damage mechanics and crystal plasticity model and its application in damage evolution in polycrystalline aggregates

Cited by 23 publications

References 69 publications

ANFIS-based forming limit prediction of stainless steel 316 sheet metals

ANFIS-based forming limit prediction of stainless steel 316 sheet metals

Full‐Field Numerical Simulation of Halite Dynamic Recrystallization From Subgrain Rotation to Grain Boundary Migration

On the wave dispersion of a multi-scale hybrid nanocomposite microstructure via deep neural network technique

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