In Situ Experiments: Paving Ways for Rapid Development of Structural Metallic Materials for a Sustainable Future

Sahu, Vivek; Sonkusare, Reshma; Biswas, Krishanu; Gurao, N.P.

doi:10.1007/s41745-022-00292-2

Cited by 3 publications

(6 citation statements)

References 126 publications

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“…1a). The details are provided elsewhere [8]. The deformation was performed in two-phase region as obtained from CALPHAD [10].…”

Section: Methodsmentioning

confidence: 99%

“…Microstructural investigation was carried out using field emission-scanning electron microscope (FE-SEM) equipped with electron backscatter diffraction (EBSD) detector. The samples were prepared metallographically [8], by polishing with emery paper and alumina cloth followed by colloidal silica polishing. EBSD scans were acquired with a step size of 0.2 μm.…”

Section: Methodsmentioning

confidence: 99%

“…In the present investigation, four representative samples at extreme temperature and strain rates were selected to study the texture evolution, using electron backscatter diffraction (EBSD), neutron diffraction and viscoplastic self-consistent (VPSC) simulations, to understand the fundamental microstructural development of the alloy during hot compression. The extreme temperature and strain rate values are based on the processing map of the CoCuFeMnNi CCA, studied earlier by the authors [8].…”

Section: Abstract Complex Concentrated Alloys • Hightemperature Defor...mentioning

confidence: 99%

“…Neutron diffraction is a standard and efficient bulk texture measurement technique for polycrystalline materials [7,8]. Neutrons interact weakly with the matter and hence have high penetration depth; 10 2 -10 3 times more than laboratory X-rays [9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Texture Evolution During Hot Compression of CoCuFeMnNi Complex Concentrated Alloy Using Neutron Diffraction and Crystal Plasticity Simulations

Sonkusare

Biswas

Gan

et al. 2022

Trans Indian Inst Met

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“…1a). The details are provided elsewhere [8]. The deformation was performed in two-phase region as obtained from CALPHAD [10].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Abstract Complex Concentrated Alloys • Hightemperature Defor...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Texture Evolution During Hot Compression of CoCuFeMnNi Complex Concentrated Alloy Using Neutron Diffraction and Crystal Plasticity Simulations

Sonkusare

Biswas

Gan

et al. 2022

Trans Indian Inst Met

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“…Traditional trial-and-error experimental methods waste time and energy, so computationally guided experiments have gradually become a research hotspot since the Materials Genome Initiative (MGI) was proposed [1][2]. Among those high-throughput methods, machine learning methods have become the focus of MGI research due to their fast and accurate performance [3][4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Artificial Neural Network Elastic Constant Prediction Method Based on Particle Swarm Optimization

Wei¹,

Wang²,

Li³

2022

J. Phys.: Conf. Ser.

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As machine learning methods keep developing in materials science, using machine learning to mine novel materials has become a research hotspot. For microscopic crystals, the first-principles calculation of elastic constants has always been a conventional research method. However, the first-principles calculation method is time-consuming and laborious, which limits the development of materials. In order to achieve efficient discovery of new materials, this paper proposed an improved artificial neural network (ANN) method to predict the elastic modulus of crystals improved by particle swarm optimization (PSO), which build the PSO-ANN model in use of structures and elastic constants of crystals. Experiments show that the root mean square error (RMSE) can reach within 1GPa. To verify the accuracy of the method for other crystal predictions, the MgZn2 crystal is modelled and calculated by using the first-principles calculation method. Comparing with the prediction method proposed, the average error of the predicted elastic constants is within the range 5.6%.

show abstract

Insight into the Fe-rich phases strengthening mechanisms of non-heat-treatable Al-Mg-Mn-Fe-Cu alloys

Zhao,

He,

Zhao

et al. 2025

Journal of Materials Science & Technology

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In Situ Experiments: Paving Ways for Rapid Development of Structural Metallic Materials for a Sustainable Future

Cited by 3 publications

References 126 publications

Texture Evolution During Hot Compression of CoCuFeMnNi Complex Concentrated Alloy Using Neutron Diffraction and Crystal Plasticity Simulations

Texture Evolution During Hot Compression of CoCuFeMnNi Complex Concentrated Alloy Using Neutron Diffraction and Crystal Plasticity Simulations

An Improved Artificial Neural Network Elastic Constant Prediction Method Based on Particle Swarm Optimization

Insight into the Fe-rich phases strengthening mechanisms of non-heat-treatable Al-Mg-Mn-Fe-Cu alloys

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