A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary ...A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.展开更多
To further investigate the forming mechanism and springback characteristics of strips under multi-square punch forming (MSPF) considering partial-unloading effects, a series of concave form ing tests of strips are con...To further investigate the forming mechanism and springback characteristics of strips under multi-square punch forming (MSPF) considering partial-unloading effects, a series of concave form ing tests of strips are conducted on the MSPF machine. This paper aims to reveal the physical mecha nism of the elastic-plastic deformation in the MSPF process considering the effect of the forming ap proaches, and derive appropriate mathematical interpretations. The theoretical model is firstly estab lished to analyse the concave forming mechanism and springback characteristics of the strip, and its accuracy is then validated by experimental data. The forming history and load evolutions are depicted to explore the required forming capacity through the proposed analytical method. Besides, the paramet ric studies are carried out to discuss their effects on the springback of the strip. The results suggest that the deformation paths of the strip are influenced by the forming approach, and the springback of the strip in convex forming is larger than that in concave forming.展开更多
In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath f...In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.展开更多
In order to clarify the influence of grain size on cyclic deformation response of superalloy sheets and springback behavior,cyclic loading-unloading and shearing tests were performed on the superalloy foils with 0.2 m...In order to clarify the influence of grain size on cyclic deformation response of superalloy sheets and springback behavior,cyclic loading-unloading and shearing tests were performed on the superalloy foils with 0.2 mm in thickness and diverse grain sizes.The results show that,the decline ratio of elastic modulus is weakened with increasing grain size,and the Bauschinger effect becomes evident with decreasing grain size.Meanwhile,U-bending test results determine that the springback is diminished with increasing grain size.The Chaboche,Anisotropic Nonlinear Kinematic(ANK)and Yoshida-Uemori(Y-U)models were utilized to fit the shear stress-strain curves of specimens.It is found that Y-U model is sufficient of predicting the springback.However,the prediction accuracy is degraded with increasing grain size.展开更多
Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback predicti...Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback prediction in stretch bending forming mainly focuses on artificial neural networks combined with the finite element simulation. There is a lack of springback prediction by support vector regression（SVR）. In this paper, SVR is applied to predict springback in the three-dimensional stretch bending forming process, and variable stretch force trajectory is optimized. Six parameters of variable stretch force trajectory are chosen as the input parameters of the SVR model. Sixty experiments generated by design of experiments（DOE） are carried out to train and test the SVR model. The experimental results confirm that the accuracy of the SVR model is higher than that of artificial neural networks. Based on this model, an optimization algorithm of variable stretch force trajectory using particle swarm optimization（PSO） is proposed. The springback amount is used as the objective function. Changes of local thickness are applied as the criterion of forming constraints. The objection and constraints are formulated by response surface models. The precision of response surface models is examined. Six different stretch force trajectories are employed to certify springback reduction in the optimum stretch force trajectory, which can efficiently reduce springback. This research proposes a new method of springback prediction using SVR and optimizes variable stretch force trajectory to reduce springback.展开更多
A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) w...A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established. Based on the model, the calculation errors of springback can be controlled within 10% compared with the experimental results.展开更多
基金supported by the National Key R&D Program of China(No.2021YFB3400900)the National Natural Science Foundation of China(Nos.52175373,52205435)+1 种基金Natural Science Foundation of Hunan Province,China(No.2022JJ40621)the Innovation Fund of National Commercial Aircraft Manufacturing Engineering Technology Center,China(No.COMACSFGS-2022-1875)。
文摘A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.
文摘To further investigate the forming mechanism and springback characteristics of strips under multi-square punch forming (MSPF) considering partial-unloading effects, a series of concave form ing tests of strips are conducted on the MSPF machine. This paper aims to reveal the physical mecha nism of the elastic-plastic deformation in the MSPF process considering the effect of the forming ap proaches, and derive appropriate mathematical interpretations. The theoretical model is firstly estab lished to analyse the concave forming mechanism and springback characteristics of the strip, and its accuracy is then validated by experimental data. The forming history and load evolutions are depicted to explore the required forming capacity through the proposed analytical method. Besides, the paramet ric studies are carried out to discuss their effects on the springback of the strip. The results suggest that the deformation paths of the strip are influenced by the forming approach, and the springback of the strip in convex forming is larger than that in concave forming.
基金Project(50175034) supported by the National Natural Science Foundation of China
文摘In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.
基金the National Natural Science Foundation of China(Nos.51975031,52075023,51635005)Defense Industrial Technology Development Program,China(No.JCKY2018601C207)。
文摘In order to clarify the influence of grain size on cyclic deformation response of superalloy sheets and springback behavior,cyclic loading-unloading and shearing tests were performed on the superalloy foils with 0.2 mm in thickness and diverse grain sizes.The results show that,the decline ratio of elastic modulus is weakened with increasing grain size,and the Bauschinger effect becomes evident with decreasing grain size.Meanwhile,U-bending test results determine that the springback is diminished with increasing grain size.The Chaboche,Anisotropic Nonlinear Kinematic(ANK)and Yoshida-Uemori(Y-U)models were utilized to fit the shear stress-strain curves of specimens.It is found that Y-U model is sufficient of predicting the springback.However,the prediction accuracy is degraded with increasing grain size.
基金Supported by National Technical Innovation Foundation of China(Grant No.Jilin Province 350)
文摘Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback prediction in stretch bending forming mainly focuses on artificial neural networks combined with the finite element simulation. There is a lack of springback prediction by support vector regression（SVR）. In this paper, SVR is applied to predict springback in the three-dimensional stretch bending forming process, and variable stretch force trajectory is optimized. Six parameters of variable stretch force trajectory are chosen as the input parameters of the SVR model. Sixty experiments generated by design of experiments（DOE） are carried out to train and test the SVR model. The experimental results confirm that the accuracy of the SVR model is higher than that of artificial neural networks. Based on this model, an optimization algorithm of variable stretch force trajectory using particle swarm optimization（PSO） is proposed. The springback amount is used as the objective function. Changes of local thickness are applied as the criterion of forming constraints. The objection and constraints are formulated by response surface models. The precision of response surface models is examined. Six different stretch force trajectories are employed to certify springback reduction in the optimum stretch force trajectory, which can efficiently reduce springback. This research proposes a new method of springback prediction using SVR and optimizes variable stretch force trajectory to reduce springback.
文摘A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established. Based on the model, the calculation errors of springback can be controlled within 10% compared with the experimental results.
