Advances in friction of aluminium alloy deep drawing

Gao, Yiren; Li, Hongxia; Zhao, Danyang; Wang, Minjie; Fan, Xiaobo

doi:10.1007/s40544-023-0761-7

Cited by 3 publications

(2 citation statements)

References 158 publications

(231 reference statements)

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“…Research projects on the influence of parameters such as blocking force and die geometry on the quality of stamping products, such as limiting earing height, wrinkling, and tearing [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. Research on the effects of friction to improve drawing efficiency has been published [17], [18], [19], [20], [21], [22], [23], [24], [25]. The influence of material parameters on the quality of stamping products has been reported [26].…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Drawing Technology for Tiny Parts Applied in the Electrical-Electronic Industry

Duy,

Anh,

Sinh

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Meso-and microforming is a technology to shape parts from extremely small metal billets. Parts with geometric dimensions are a few millimetres to a few micrometres. With the rapid development of the electrical-electronics industry and biomedical engineering, the technology of forming microscopic parts has been researched and applied because of its efficiency, accuracy, and high productivity. Deep drawing is an operation that turns flat sheet metal blanks into hollow, open-mouth parts. It is an essential process in sheet metal stamping. Micro deep drawing is one of the micro-shaping technologies that has been widely studied and applied in recent years. However, the bases for calculating technological and geometrical parameters in the micro-deep drawing have not yet been analyzed and evaluated in detail. Therefore, this paper has proposed a theoretical basis combined with simulation applied to the design of technology to manufacture a connector head part drawing die with a diameter of 300µm and height of 1500µm using materials SUS304 material. Numerical simulation also allows evaluation of the stamping part's internal stress state, the ability to pull the workpiece into the die, and the thickness distribution on the product wall. Experimental research has also verified that, with the determined parameters, the stamping parts meet the quality requirements. This indicates the proposed calculation methods for the tiny deep drawing operation are entirely suitable. The results of this research can be wholly applied to the production of micro-sized cylindrical cup parts using the deep drawing method.

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Section: Introductionmentioning

confidence: 99%

Research on Deep Drawing Technology for Tiny Parts Applied in the Electrical-Electronic Industry

Duy,

Anh,

Sinh

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The BHF trajectory is optimized to ensure smooth material flow, preventing local thinning and wrinkling [ 10 , 11 ]; a related basic schematic diagram is shown in Figure 1 . Moreover, appropriate lubrication could increase sheet material flow to reduce defects [ 12 ]. Yet, excessively low friction might result in unintended issues like tearing or excessive thinning [ 13 , 14 ].…”

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

Guo,

Jeong,

Park

et al. 2024

Int J Adv Manuf Technol

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Advances in friction of aluminium alloy deep drawing

Cited by 3 publications

References 158 publications

Research on Deep Drawing Technology for Tiny Parts Applied in the Electrical-Electronic Industry

Research on Deep Drawing Technology for Tiny Parts Applied in the Electrical-Electronic Industry

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

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

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