The Square Cup Deep Drawing by Variable Blank Holder Force(Machine Elements, Design and Manufacturing)

In press processing, quality inspection of a product is often carried out for each lot in the post process stage. When a failure occurs, it may result in a large number of defective products due to the fast processing speed. In order to prevent this, it is ideal to immediately stop the processing just after the defect occurs. Therefore, confirming the processing state in-process is required. This study proposes a new quality inspection method for deep drawing processes by using the count rate of acoustic emission (AE) signals. To analyze the AE count, deep learning, which is a multilayered neural network, is employed to recognize defects during a deep drawing process. The material used was a ductile material of cold rolled steel plate and is relatively difficult to find cracks during the deep drawing process. Characteristics were clarified by analysis of AE counts at plastic deformation and brake of material, and performing the forming state recognition experiment by a multilayer neural network (deep learning) showed a maximum recognition rate of 97.3%. High recognition rate was obtained despite the small number of data used.

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Optimum Trajectory of the Variable Blank Holder Force in the Square Cup Deep Drawing

Kitayama

Kita²,

Yamazaki³

2011

Trans.JSME(C)

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In deep drawing, a low blank holder force (BHF) will cause wrinkling, while a high BHF will lead to tearing. The Variable Blank Holder Force (VBHF) is an attractive approach in the industries, and it is important to determine the VBHF trajectory. In this paper, a sequential approximate optimization (SAO) approach is employed for determining the VBHF trajectory. The objective is taken as the minimization of the deviation of whole thickness. The constraint is quantitatively defined with the Forming Limit Diagram (FLD), in which no wrinkling and tearing can be observed. The blank holder forces are taken as the design variables. Two constraints in which wrinkling and tearing are avoided are employed in this paper. The Radial Basis Function (RBF) network is employed for constructing the response surface to capture the non-linear phenomena. In the proposed SAO approach, the optimum BHF is added to refine the VBHF trajectory, while some data points are also added to cover the entire design variable space. The result of the VBHF trajectory with the proposed SAO approach is compared with the one of the closed-loop type algorithm.

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The Square Cup Deep Drawing by Variable Blank Holder Force(Machine Elements, Design and Manufacturing)

Cited by 5 publications

References 16 publications

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Mechanical servo press technology for metal forming

Forming state recognition in deep drawing process with machine learning

Optimum Trajectory of the Variable Blank Holder Force in the Square Cup Deep Drawing

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