Simultaneous Optimization of Variable Blank Holder Force Trajectory and Tools Motion in Deep Drawing via Sequential Approximate Optimization

Srirat, Jirasak; Kitayama, Satoshi; Yamazaki, Koetsu

doi:10.1299/jamdsm.6.1081

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Research in recent years has been leaning toward advanced optimization methods, as listed in Table 1 . Neural network cooperative interaction with genetic algorithms was used by Srirat et al [ 15 ], Li et al [ 16 ], Kitayama et al [ 17 , 18 , 19 ], and Tran et al [ 20 ]. However, some more practical and in-depth conclusions need to be drawn, instead of just a result parameter, to help engineering adjust process parameters in specific cases.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the estimation of material processability is crucial in assessing the workability of sheet metals and identifying product issues in forming processes. The Forming Limit Diagram (FLD) was commonly utilized in experimental settings to assess the formability of manufactured components [ 10 , 15 , 17 , 18 , 19 , 23 , 24 ]. Keeler and Backofen [ 38 ] and Goodwin [ 39 ] are acknowledged as trailblazers in the initial development of the FLD.…”

Section: Introductionmentioning

confidence: 99%

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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%

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

View full text Add to dashboard Cite

show abstract

“…Approximate model is an efficient research method to reduce the time consumption. Many kinds of approximate model are able to achieve the approximation of bending springback process, such as artificial neural network [8][9][10][11], radial basis function [12,13], and response surface method [14,15]. The error backpropagation neural network (BPNN) is one of the widely used and successful networks at present.…”

Section: Introductionmentioning

confidence: 99%

Bending Angle Prediction Model Based on BPNN‐Spline in Air Bending Springback Process

Guo

Tang

2017

Mathematical Problems in Engineering

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In order to rapidly and accurately predict the springback bending angle in V-die air bending process, a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline) is presented in this study. An orthogonal experimental sample set for training BPNN-Spline is obtained by finite element simulation. Through the analysis of network structure, the BPNN-Spline black box function of bending angle prediction is established, and the advantage of BPNN-Spline is discussed in comparison with traditional BPNN. The results show a close agreement with simulated and experimental results by application examples, which means that the BPNN-Spline model in this study has higher prediction accuracy and better applicable ability. Therefore, it could be adopted in a numerical control bending machine system.

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“…Some studies show the results of recent effort in developing efficient schemes (Wang and Chang, 2004;Hirani and Suh, 2005;Wang, 2005;Bhat and Barrans, 2008;Wang and Cha, 2010;Lu and Xie, 2014) for solving multiobjective optimization problems (MOOPs). To minimize the execution time many of these optimization analyses were carried out by using parallel computing or approximating the computationally intensive function with a surrogate model (Li, et al, 2008;Srirat, et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Multiobjective design of porous air bearing using group inching fortification method

Wang

Chen

2015

JAMDSM

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This study presents a new approach, group inching fortification (GIF) method, to deal with multiobjective optimization problems found in various mechanical designs. The GIF method is exemplified by a four-factor porous air bearing design. In the GIF method the initial group of designs in Pareto rank 1 is used as the basis to inch up the formation of Pareto solution set, which is fortified over the search process by uniting superior or non-dominated solutions from base-point exploration moves. In this study, a comparison of the GIF method with genetic algorithm (GA) and hyper-cube dividing method (HDM) for the same air bearing design is presented. The results show that the Pareto solution set obtained by the GIF method has more design selections with a wider coverage (breadth). Equally important, the number of objective-function calls required (179, 736, and 3600 for GIF, HDM, and GA, respectively) in the GIF method is significantly reduced. In this study, the GIF method is suggested to be terminated when all the designs are non-dominated by each other, a criterion which is very difficult to achieve by using the GA and HDM. This study proposes an effective design tool which is easy-to-implement for solving the problems with multiple objectives.

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Simultaneous Optimization of Variable Blank Holder Force Trajectory and Tools Motion in Deep Drawing via Sequential Approximate Optimization

Cited by 4 publications

References 20 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

Bending Angle Prediction Model Based on BPNN‐Spline in Air Bending Springback Process

Multiobjective design of porous air bearing using group inching fortification method

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