Springback optimization of deep drawing process based on FEM-ANN-PSO strategy

Mrabti, Iliass El; Touache, Abdelhamid; Hakimi, Abdelhadi El; Chamat, A.

doi:10.1007/s00158-021-02861-y

Cited by 24 publications

(7 citation statements)

References 33 publications

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“…Mrabti et al, 2021 [20] prioritized the degree of importance of establishing training sets and correlating critical process parameters to springback using artificial neural network (ANN) models. To identify the optimal process parameters, particle swarm optimization (PSO) was subsequently employed.…”

Section: Figure 1 Schematic Diagram Of Constant Bhf and S-vbhf In Dee...mentioning

confidence: 99%

“…By increasing the drawn wall region thickness predicted by the optimized BHF, the deep-drawability could be improved. Mrabti et al 2021 [20] used the ANOVA method to evaluate the importance degree of process parameters as constant blank holder force (CBHF), punch velocity (v p ), friction of die (μ d ) and friction of holder (μ h ) on springback. The authors used artificial neural network (ANN) model to set training sets and related process parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

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Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy

Guo¹,

Jeong²,

Park³

et al. 2023

Preprint

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This research introduces a novel methodology for mitigating defects in sheet metal forming processes. The proposed approach employs a segmented and variable blank holder force (S-VBHF) trajectory, adjusting the blank holder force (BHF) during the forming cycle, enhancing formability, and reducing failure, wrinkling and springback defects. Optimal process parameters, including the S-VBHF, friction coefficient and drawbead restraining force (DBRF), were determined through a systematic methodology integrating deep neural network, genetic algorithm and Monte Carlo simulation (DNN-GA-MCS) techniques. The design constraint, defined as sheet failure during the forming process, was quantitatively evaluated using the forming limit diagram (FLD) to ensure rigorous assessment. The proposed methodology was validated through numerical simulations using a cylindrical cup provided by NUMISHEET 2011 (BM1) as test samples. The simulation results demonstrated a significant improvement in the formed sheet quality, characterized by reductions of 8.33%, 10.81% and 5.88% in failure, wrinkling and springback defects, respectively. These findings underscore the potential of the proposed approach in enhancing the quality of sheet metal forming processes and mitigating defects.

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Section: Figure 1 Schematic Diagram Of Constant Bhf and S-vbhf In Dee...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy

Guo¹,

Jeong²,

Park³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The results showed that the springback and longitudinal arch were reduced by using the proposed mold design method. El et al [22] took the deep drawing process as the research object, and proposed an optimization method combining finite element, experimental, artificial neural network and particle swarm optimization to optimize the quality of stamped parts, especially to solve the springback problem. In order to optimize the stamping forming quality of high-strength steel, Jia et al [23] used the B-Benhnken test to construct a multi-objective optimization function of the response surface between the maximum springback displacement and the maximum thinning ratio process parameters.…”

Section: Introductionmentioning

confidence: 99%

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

Cao

Liu

et al. 2023

Metals

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Roll-bending technology has a high degree of flexibility and does not require special molds. However, based on the existing plastic mechanics theory and finite element simulation, it is difficult to accurately analyze the complex spatial relationship of profile roll forming. Therefore, a fixed-curvature prediction model is constructed based on XGBoost (extreme gradient boosting), and the coupling effect of the process parameters and material performance parameters on the roll-forming process is explored. Combined with a Bayesian optimization algorithm, the hyperparameters of the fixed-curvature prediction model are optimized. In addition, based on the prediction result of the fixed curvature, a variable-curvature prediction model is established using the conditional random field (CRF). To further improve the prediction accuracy, an error compensation network is added after the result of the CRF in order to map the discrete sequence to the continuous sequence. The experimental results show that the mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) predicted by the models above are much smaller than other methods, which verifies the superiority of the prediction models.

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“…22 The bee algorithm finds a range of attributes for the deep drawing process and improves its sheet forming performance. 23 The ANN is then trained with the inputs and corresponding responses based on its significance level. Finally, PSO is utilised to find the optimal values for the significant parameters to reduce the response effect by considering multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimisation of input process parameters of deep-drawn cylindrical cups from directional rolled copper strips

Sivam

Rajendran

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Deep drawing is a method of producing sheet metal. It is extensively used in the automobile, packaging and home appliance industries. Incorrect parameter selection might induce defects in stamped components. Trial and error can improve product accuracy, but at the expense of execution and calculation. An optimisation approach and a response surface model are used to determine the optimal parametric parameter design. This study proposes a full and efficient optimisation approach, starting with modelling by RSM (Response Surface Methodology) and concluding with optimal process parameter identification. Based on this, a neural network was developed to improve cylindrical cup deep drawing. Three characteristics of fabricating the cups are clearance, punch radius and coefficient of friction. This parameter demonstrates the resultant tool force, spring back, maximum forming limiting curve and maximum thinning rate of cylinder cups. Initially, the three inputs are varied to measure the outcome using simulation software by Altair inspire form. Then, the equations for each output are designed using quadratic-based linear model fitting. The generated objective function is then used to establish the ideal process parameters for the cylindrical cups. The proposed method is compared to real-time physical experiments. The optimised parameter microstructure at the high-stress zone and the product quality are satisfactory without defects.

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Springback optimization of deep drawing process based on FEM-ANN-PSO strategy

Cited by 24 publications

References 33 publications

Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy

Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

Hybrid optimisation of input process parameters of deep-drawn cylindrical cups from directional rolled copper strips

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