A Review of the Intelligent Optimization and Decision in Plastic Forming

Tang, Xuefeng; Wang, Zhizhou; Deng, Lei; Wang, Xinyun; Long, Jinchuan; Jiang, Xin; Jin, Junsong; Xia, Juchen

doi:10.3390/ma15197019

Cited by 7 publications

(4 citation statements)

References 100 publications

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“…In addition, predictions can be obtained from a learning model, but it is not easy to derive a generalized, interpreted mathematical model for the data. Consequently, it is essential to develop an interpretable process modeling and optimization method that incorporates physical principles into the optimization method and changes the optimization process from a black box to a glass box [ 10 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction

Guo,

Jeong,

Park

et al. 2024

Materials

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An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

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Section: Introductionmentioning

confidence: 99%

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction

Guo,

Jeong,

Park

et al. 2024

Materials

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“…Developing an intelligent sensing system capable of real-time online monitoring of defect evolution during plastic deformation is of paramount importance for quality control and the realization of intelligent forming. [1][2][3][4] Currently, online sensing of metal forming primarily relies on ultrasonic, eddy current and acoustic emission (AE) detection technology. Ultrasonic detection offers advantages such as the ability to inspect large-sized components, high sensitivity, speed, and cost-effectiveness, enabling both quantitative and qualitative defect analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Developing an intelligent sensing system capable of real‐time online monitoring of defect evolution during plastic deformation is of paramount importance for quality control and the realization of intelligent forming. [ 1–4 ]…”

Section: Introductionmentioning

confidence: 99%

Intelligent Online Sensing of Defects Evolution in Metallic Materials during Plastic Deformation

Tang,

He,

Guo

et al. 2023

Advanced Intelligent Systems

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Metallic plastic deformation involves complex microstructural changes and defect evolution, posing challenges in predicting and controlling the quality and performance of formed parts. Therefore, a pressing demand exists for a proficient online defect‐sensing system to monitor defects evolution continuously within components during plastic deformation in real time. This article proposes an intelligent online sensing approach for detecting defects in metallic plastic forming based on acoustic emission (AE) and machine learning. A comparative analysis is conducted on AE amplitude signals, stress–strain curves, and defect evolution during the tensile process of TA15 titanium alloy specimens under different stress states. It is found that the defect formation process can be divided into four stages based on the AE amplitude signals. A convolutional neural network model for intelligent defect sensing is established. It leverages transfer learning and is grounded in the relationship between AE signals and the evolution of internal defects. The prediction accuracy using different pretrained models is investigated and compared. It is discerned that utilizing GoogleNet as the pretrained model offers the swiftest training pace with a prediction accuracy of 97.57%. This approach enables intelligent online sensing of internal defect evolution in metal plastic deformation processes.

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“…Under the impetus of digitalization and intellectualization tide of manufacturing, advanced simulation technology is required to accurately predict the manufacturing process and map the forming history to the product's performance [1]. The crystal plasticity (CP) model utilizes the knowledge gained from experimental and theoretical studies of deformation physics of single crystals and polycrystals to predict anisotropic deformation behavior in a natural way.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm

Yang

Tang

Deng

et al. 2023

Metals

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The accurate calibration of material parameters in crystal plasticity models is essential for applying crystal plasticity (CP) simulations. Identifying these parameters usually requires unfeasible single-crystal experiments or expensive time costs due to the use of traditional genetic algorithm (GA) optimization. This study proposed an efficient and interpretable method for calibrating the constitutive parameters with macroscopic mechanical tests. This approach utilized the Bayesian neural network (BNN)-based surrogate-assisted GA (SGA) optimization method to identify a group of constitutive parameters that can reproduce the experimental stress–strain curve and crystallographic orientation by crystal plasticity simulation. The proposed approach was performed on the calibration of typical high-entropy alloy material parameters in two different CP models. The use of the surrogate model reduces the call count of simulation in the parameter searching process and speeds up the calibration significantly. With the help of infill sampling, the accuracy of this optimization method is consistent with the CP simulation and not limited by the accuracy of the surrogate model. Another merit of this method is that the pattern that the BNN surrogate found in the model parameters can be interpreted with its integrated gradients, which helps us to understand the relationship between constitutive parameters and the output mechanical response. The interpretation of BNN can guide further experiment design to decouple particular parameters and add constraints provided by the attached experiment or prior knowledge.

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A Review of the Intelligent Optimization and Decision in Plastic Forming

Cited by 7 publications

References 100 publications

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction

Intelligent Online Sensing of Defects Evolution in Metallic Materials during Plastic Deformation

Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm

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