A mesoscale modeling methodology is proposed to predict the strain induced abnormal grain growth in the annealing process of deformed aluminum alloys. Firstly, crystal plasticity finite element(CPFE) analysis is perfo...A mesoscale modeling methodology is proposed to predict the strain induced abnormal grain growth in the annealing process of deformed aluminum alloys. Firstly, crystal plasticity finite element(CPFE) analysis is performed to calculate dislocation density and stored deformation energy distribution during the plastic deformation. A modified phase field(PF) model is then established by extending the continuum field method to consider both stored energy and local interface curvature as driving forces of grain boundary migration. An interpolation mapping approach is adopted to transfer the stored energy distribution from CPFE to PF efficiently. This modified PF model is implemented to a hypothetical bicrystal firstly for verification and then the coupled CPFE-PF framework is further applied to simulating the 2D synthetic polycrystalline microstructure evolution in annealing process of deformed AA3102 aluminum alloy.Results show that the nuclei with low stored energy embedded within deformed matrix tend to grow up, and abnormal large grains occur when the deformation is close to the critical plastic strain, attributing to the limited number of recrystallized nuclei and inhomogeneity of the stored energy.展开更多
The stress-life curve(S–N)and low-cycle strain-life curve(E–N)are the two primary representations used to characterize the fatigue behavior of a material.These material fatigue curves are essential for structural fa...The stress-life curve(S–N)and low-cycle strain-life curve(E–N)are the two primary representations used to characterize the fatigue behavior of a material.These material fatigue curves are essential for structural fatigue analysis.However,conducting material fatigue tests is expensive and time-intensive.To address the challenge of data limitations on ferrous metal materials,we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous materials.In addition,a data-augmentation framework is introduced using a conditional generative adversarial network(cGAN)to overcome data deficiencies.By incorporating the cGAN-generated data,the accuracy(R2)of the Random Forest Algorithm-trained model is improved by 0.3–0.6.It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.展开更多
Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets.Accurate roping prediction and rating are essential for industrial applications.Recently,the authors introduced an artificial n...Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets.Accurate roping prediction and rating are essential for industrial applications.Recently,the authors introduced an artificial neural network(ANN)model to efficiently forecast roping behavior across the thickness of large regions with texture gradients.In this study,the previously proposed ANN model for roping prediction is briefly reviewed,and a few-shot learning(FSL)-based method is developed for roping grading with limited samples.To consider the directionality of the roping patterns,the roping dataset constructed from experimental observations is transformed into the frequency domain for more compact characterization.A transfer-based FSL method is further presented for grade roping with manifold mixup regularization and the Sinkhorn mapping algorithm.A new component-focused representation is also implemented for data-processing,exploiting the close correlation between roping and power distribution in the frequency domain.The ultimate FSL method achieved an optimal accuracy of 95.65%in roping classification with only five training samples per class,outperforming four typical FSL methods.This FSL approach can be applied to grade the roping morphologies predicted by the ANN model.Consequently,the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.展开更多
基金the financial support from the National Natural Science Foundation of China (Nos. U2141215, 52105384 and 52075325)the support of Materials Genome Initiative Center, Shanghai Jiao Tong University, China。
文摘A mesoscale modeling methodology is proposed to predict the strain induced abnormal grain growth in the annealing process of deformed aluminum alloys. Firstly, crystal plasticity finite element(CPFE) analysis is performed to calculate dislocation density and stored deformation energy distribution during the plastic deformation. A modified phase field(PF) model is then established by extending the continuum field method to consider both stored energy and local interface curvature as driving forces of grain boundary migration. An interpolation mapping approach is adopted to transfer the stored energy distribution from CPFE to PF efficiently. This modified PF model is implemented to a hypothetical bicrystal firstly for verification and then the coupled CPFE-PF framework is further applied to simulating the 2D synthetic polycrystalline microstructure evolution in annealing process of deformed AA3102 aluminum alloy.Results show that the nuclei with low stored energy embedded within deformed matrix tend to grow up, and abnormal large grains occur when the deformation is close to the critical plastic strain, attributing to the limited number of recrystallized nuclei and inhomogeneity of the stored energy.
基金support provided by the Jiangsu Industrial Technology Research Institute and the Yangtze Delta Region Institute of Advanced Materialssupported by the National Natural Science Foundation of China(Grant No.52205377)+1 种基金the National Key Research and Development Program(Grant No.2022YFB4601804)the Key Basic Research Project of Suzhou(Grant Nos.#SJC2022029,#SJC2022031).
文摘The stress-life curve(S–N)and low-cycle strain-life curve(E–N)are the two primary representations used to characterize the fatigue behavior of a material.These material fatigue curves are essential for structural fatigue analysis.However,conducting material fatigue tests is expensive and time-intensive.To address the challenge of data limitations on ferrous metal materials,we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous materials.In addition,a data-augmentation framework is introduced using a conditional generative adversarial network(cGAN)to overcome data deficiencies.By incorporating the cGAN-generated data,the accuracy(R2)of the Random Forest Algorithm-trained model is improved by 0.3–0.6.It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.
基金funding from the National Natural Science Foundation of China(Grant Nos.U2141215,52105384).
文摘Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets.Accurate roping prediction and rating are essential for industrial applications.Recently,the authors introduced an artificial neural network(ANN)model to efficiently forecast roping behavior across the thickness of large regions with texture gradients.In this study,the previously proposed ANN model for roping prediction is briefly reviewed,and a few-shot learning(FSL)-based method is developed for roping grading with limited samples.To consider the directionality of the roping patterns,the roping dataset constructed from experimental observations is transformed into the frequency domain for more compact characterization.A transfer-based FSL method is further presented for grade roping with manifold mixup regularization and the Sinkhorn mapping algorithm.A new component-focused representation is also implemented for data-processing,exploiting the close correlation between roping and power distribution in the frequency domain.The ultimate FSL method achieved an optimal accuracy of 95.65%in roping classification with only five training samples per class,outperforming four typical FSL methods.This FSL approach can be applied to grade the roping morphologies predicted by the ANN model.Consequently,the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.
