Optimization of Variable Blank Holder Force Based on a Sharing Niching RBF Neural Network and an Improved NSGA-II Algorithm

Xie, Yanmin; Tang, Wei; Zhang, Fei; Pan, BeiBei; Yue, Yaopeng; Feng, Meiqiang

doi:10.1007/s12541-019-00019-x

Cited by 21 publications

(7 citation statements)

References 18 publications

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“…There are many reasons for the cracking of sheet hydroforming, while FLD describes ultimate strain of sheet's cracking under different conditions. When the point's principal strain falls above the forming limit curve (FLC), 4,30,31 the part will break when it is formed. When the point's principal strain is below the FLC curve, the farther away the point is, the safer it is.…”

Section: Determination Of Design Variablesmentioning

confidence: 99%

“…The results showed that compared with the static blank holder, the pulsating blank holder could significantly improve the limit drawing ratio of the drawing process by approximately 11.53%. Xie et al 4 employed a VBHF path with a step change of the constant blank holder force. The results showed that this type of blank holder force could reduce wrinkles on the flange and bottom.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Zhang

Zhou

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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As a critical component of the propellant tank, the tank bottom is subjected to complex loads such as internal pressure and vibration and has high requirements for structural load-bearing capacity. Hydroforming deep drawing is one of the techniques for the integral forming of the tank bottom. As the tank bottom is a large-size thin-walled structure, defects such as cracks and wrinkles are prone to occur during the hydroforming deep drawing process. Aiming at reducing these defects, the hydraulic pressure loading path and blank holder force loading path of the hydroforming deep drawing process are studied, and a multi-objective optimization method is proposed to improve the surface accuracy and thickness distribution uniformity of the tank bottom. The complex loading path curve optimization problem is transformed into a functional relationship between hydraulic pressure and blank holder force with time. The hydraulic pressure and blank holder force at each time node are used as design variables, and the maximum wall thickness reduction rate, rupture trend factor, wrinkle height, and wrinkle trend factor are used as optimization targets. The radial basis function (RBF) neural network is used to establish the approximate model between the loading path and the optimization target, and the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the solution. Taking the hemispherical tank bottom as an example, the optimal hydraulic pressure loading path and blank holder force loading path are obtained, and the quality of the formed part is improved.

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Section: Determination Of Design Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Zhang

Zhou

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Radial basis function (RBF) neural network [38]- [40] is a three-layer feedforward network, the mapping from input to output is nonlinear, but the mapping from hidden layer to output layer is linear. Thus, the learning speed is greatly accelerated and the local minimum problem is avoided.…”

Section: A Radial Basis Function Neural Networkmentioning

confidence: 99%

Tuning of ADRC for QTR in Transition Process Based on NBPO Hybrid Algorithm

Wang

Duan

et al. 2019

IEEE Access

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The quad tilt rotor (QTR) has complex dynamics characteristics, and the environmental factors have a great influence on it. This increases the difficulty of its control especially in transition mode. To solve this problem, the design of the controller based on active disturbance rejection control (ADRC) with a novel tuning algorithm is proposed in this paper. The aerodynamic models of propeller, wing, vertical tail and fuselage are build respectively by using the idea of component modeling. The pitch channel of the linearized flight dynamic model is provided for the control system. A simple tuning method of active disturbance rejection control for the quad tilt rotor in transition process that achieves high performance and good robustness is presented. The proposed method makes ADRC become easy to tune and more practical. The problem of parameter tuning is turned into a multi-objective optimization problem, and using radial basis function(RBF) neural network with particle swarm optimization algorithm(PSO) and bacterial foraging optimization algorithm(BFO) hybrid algorithm tuning method(NBPO) to fit the tuning rules. We introduce the control input, the overshoot and rise time of the system into the fitness function. The local and global optimal solutions are introduced into the chemotaxis and migration to improve the information exchange ability of BFO. The correctness and effectiveness of the NBPO tuning method were verified by numerical simulation results, compared with the one parameter tuning method(OPM) and PID, the results showed that the NBPO tuning method can greatly improve the control performance of the system.

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“…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. Researchers, including Feng et al [ 21 , 22 ], Zhai et al [ 23 ], Xie et al [ 24 ], Taşkın et al [ 25 ], Jiang et al [ 26 ], Yu et al [ 27 ], and Guo et al [ 10 ], have enriched this field by merging an area of design techniques incorporating the latest developments. Implementing these advanced computational techniques in the context of sheet metal forming represents a convergence of machine learning, optimization algorithms, and computational physics, but promising substantial addresses of the complex, nonlinear, and stochastic nature of the metal forming process need more substantial results and discussion.…”

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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Optimization of Variable Blank Holder Force Based on a Sharing Niching RBF Neural Network and an Improved NSGA-II Algorithm

Cited by 21 publications

References 18 publications

Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Multi-objective optimization of loading path for sheet hydroforming of tank bottom

Tuning of ADRC for QTR in Transition Process Based on NBPO Hybrid Algorithm

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

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