Control of Defects in Deep Drawing of Tailor-welded Blanks for Complex Shape Automotive Panel

Abstract

With the development of lightweight vehicles, tailor welded blanks (TWBs) are increasingly used in the automotive industry. Splitting and wrinkling are the main defects during the deep drawing of TWBs. A new method to control the forming defects was introduced in the forming process of TWBs in this study. The microstructure and mechanical properties of TWBs were characterized through metallography and tensile tests. Finite element modelling of an automobile rear door inner panel made of TWBs was built to analyse deep drawing. Edge cutting and notch cut were introduced in the drawing to deal with forming defects and reduce the number of stamping tools. The minimum distance between the material draw-in and trimming lines, thinning index and thickening index were defined as the measurable index to analyse the numerical results. Orthogonal experiment, numerical simulation and multiobjective experiment were utilised to optimize the forming parameters. The proposed method and optimised parameters were verified through experiments. The experimental results are basically consistent with the numerical simulation. Results demonstrate that the proposed method can provide some guidance for controlling the defects in deep drawing of TWBs for complex shape automotive panel.

Fabrication of Hollow Needle Tips with Nanoholes by Plasma Maskless Processing and Calculation and Detection of Complex Surface Edges

“…However, the roughness of the line edge is large, which makes this technology need more refined technology to improve. Because the complex surface shape is complex and difficult to operate, Wang et al introduced a new method to control the forming defects of the complex surface shape, introducing edge and notch cutting to solve the forming defects and reduce the number of stamping tools [3]. However, his method is difficult to be uniformly applicable to different models, and cannot be applied in batches.…”

HDR Image-Based Deep Learning Approach for Automatic Detection of Split Defects on Sheet Metal Stamping Parts

“…The stamping process is particularly suited for high-volume mass production (typically tens of parts per minute). As a result, despite the high investment costs of tooling (of the order of £250k per toolset), the per piece price is relatively low (compared to other processes such as casting or machining) because production costs are amortised over large volumes [2]. Furthermore, it allows the formation of complex shapes.…”

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