Multi-objective parameter identification and optimization for dislocation-dynamics-based constitutive modeling of Ti–6Al–4V alloy

Wang, Yuntian; Zeng, Xiangguo; Sheng, Ying; Yang, Xin; Wang, Fang

doi:10.1016/j.jallcom.2019.153460

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…This method is mainly used to identify the material properties of non-homogeneous materials, such as composites [ 29 , 30 ], layered structures [ 26 , 31 ], bones [ 16 ], or soil [ 32 ]. On the other hand, in [ 33 , 34 ] the authors used optimization to identify the parameters of the homogeneous metallic alloy. In most cases, evolutionary algorithms were used to efficiently derive material parameters using a metamodel-based strategy [ 29 , 30 ] or direct optimization without surrogate models [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Correlation of Bone Material Model Using Voxel Mesh and Parametric Optimization

et al. 2022

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The authors present an algorithm for determining the stiffness of the bone tissue for individual ranges of bone density. The paper begins with the preparation and appropriate mechanical processing of samples from the bovine femur and their imaging using computed tomography and then processing DICOM files in the MIMICS system. During the processing of DICOM files, particular emphasis was placed on defining basic planes along the sides of the samples, which improved the representation of sample geometry in the models. The MIMICS system transformed DICOM images into voxel models from which the whole bone FE model was built in the next step. A single voxel represents the averaged density of the real sample in a very small finite volume. In the numerical model, it is represented by the HEX8 element, which is a cube. All voxels were divided into groups that were assigned average equivalent densities. Then, the previously prepared samples were loaded to failure in a three-point bending test. The force waveforms as a function of the deflection of samples were obtained, based on which the global stiffness of the entire sample was determined. To determine the stiffness of each averaged voxel density value, the authors used advanced optimization analyses, during which numerical analyses were carried out simultaneously, independently mapping six experimental tests. Ultimately, the use of genetic algorithms made it possible to select a set of stiffness parameters for which the error of mapping the global stiffness for all samples was the smallest. The discrepancies obtained were less than 5%, which the authors considered satisfactory by the authors for such a heterogeneous medium and for samples collected from different parts of the bone. Finally, the determined data were validated for the sample that was not involved in the correlation of material parameters. The stiffness was 7% lower than in the experimental test.

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Section: Introductionmentioning

confidence: 99%

Correlation of Bone Material Model Using Voxel Mesh and Parametric Optimization

et al. 2022

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“…The soil intrinsic model is obtained, through which the geotechnical engineering can be better analyzed and designed. A large number of scholars and experts have conducted numerous researches on soil constitutive mode [3][4][5][6][7][8][9]. However, the soil is complex, so there are some limitations for each model to accurately identify and classify it.…”

Section: Introductionmentioning

confidence: 99%

Parameter Determination Method of Soil Constitutive Model Based on Machine Learning

Song

Liu

Jiang

et al. 2022

Wireless Communications and Mobile Computing

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In order to better determine the model parameters and improve the safety and stability of construction, an optimized identification method of constitutive model parameters with improved RCGA is proposed by combining the current optimization theory. The experimental results indicate that the improved RCGA algorithm proposed in the study has stronger recognition capabilities and optimization effects than the traditional NSGA-II algorithm and determine the parameters of the constitutive model more accurately. Moreover, it is found that the parameters obtained by the proposed improved algorithm have validity and accuracy, when parameter P 0 ′ = 50 kPa, comparing the obtained optimal parameters with the real laboratory clay parameters.

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High-Temperature Oxidation Behavior of Al-Modified Boronized Coating Prepared on Ti-6Al-4V by Thermal Diffusion

Liu

Xiao

et al. 2020

J. of Materi Eng and Perform

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Multi-objective parameter identification and optimization for dislocation-dynamics-based constitutive modeling of Ti–6Al–4V alloy

Cited by 5 publications

References 54 publications

Correlation of Bone Material Model Using Voxel Mesh and Parametric Optimization

Correlation of Bone Material Model Using Voxel Mesh and Parametric Optimization

Parameter Determination Method of Soil Constitutive Model Based on Machine Learning

High-Temperature Oxidation Behavior of Al-Modified Boronized Coating Prepared on Ti-6Al-4V by Thermal Diffusion

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