Numerical and statistical analysis of aluminum deep drawing using LS-DYNA coupled with Taguchi design and response surface methodology

TL;DR

This study uses simulations to analyze how different parameters affect the deep drawing process of aluminum sheets.

Contribution

The novel contribution is combining Taguchi design and RSM to optimize deep drawing of aluminum with LS-DYNA simulations.

Findings

Punch velocity is the most significant factor affecting thinning and forming force in aluminum deep drawing.

Regression models developed using RSM show strong reliability and predictive accuracy.

Blank holder force has a minor impact compared to punch velocity and blank thickness.

Abstract

Deep drawing is one of the most widely used sheet metal forming processes in the manufacturing industry. This study presents a finite element-based investigation of the deep drawing process of aluminum circular blanks using LS-DYNA. The objective is to assess the influence of key process parameters on formability and failure modes during low-depth cup forming. A parametric design based on the Taguchi method was adopted, incorporating three levels for each of the following parameters: punch velocity (2, 5, 8 mm/s), blank thickness (1, 1.5, 2 mm), and blank holder force (5, 10, 15 kN). The output responses included maximum forming force, maximum thinning %, effective plastic strain, Von Mises stress, and Forming Limit Diagram (FLD) values. The simulation results were analyzed using ANOVA within Response Surface Methodology (RSM) to evaluate the statistical significance and interactions among the input variables. The results indicate that punch velocity is the dominant factor controlling thinning, forming force, stress, and plastic strain, followed by blank thickness, while blank holder force has a minor effect. The developed regression models exhibited strong reliability and predictive capability, with statistical validation and close agreement between predicted and actual responses.

Figures