Experimental and analytical studies on the stretch‐bending characteristics of advanced high‐strength steel sheets, primarily used in the automotive industry, are conducted. Herein, the stretch‐bending test, in which a specimen fixed at both ends is bent at its center with sharp punches until failure occurs, is conducted. Assuming that the failure of the material occurred at the maximum tensile force, a relationship between applied force and forming height is derived. Nine steels including high‐strength low‐alloy, dual‐phase, and transformation‐induced plasticity steels are tested, and the calculated results are compared with experimental limit‐forming heights. Finally, a failure criterion based on process variables and tensile properties of the material is proposed based on the deformation in the sheet thickness direction.
The punching process of AHSS induces edge cracks in successive process, limiting the application of AHSS for vehicle bodies. Controlling and predicting edge quality is substantially difficult due to the large variation in edge quality, die wear induced by high strength, and the complex effect of phase distribution. To overcome this challenge, a quality prediction model that considers the variation of the entire edge should be developed. In this study, the image of the entire edge was analyzed to provide a comprehensive evaluation of its quality. Statistical features were extracted from the edge images to represent the edge quality of DP780, DP980, and MART1500 steels. Combined with punching monitoring signals, a prediction model for hole expansion ratio was developed under punch conditions of varying clearance, punch angle, and punch edge radius. It was found that the features of grayscale variation are affected by the punching conditions and are related to the double burnish and uneven burr, which degrades the edge quality. Prediction of HER was possible based on only edge image and monitoring signals, with the same performance as the prediction based solely on punching parameters and material properties. The prediction performance improved when using all the features.
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