Cold Rolling Texture Prediction Using Finite Element Simulation with Zooming Analysis.

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Mechanics

Background:

• Cold rolling is a key manufacturing process for metal plates.
• Thickness reduction below recrystallization temperature induces significant anisotropy.
• Anisotropy affects subsequent sheet metal forming operations, making its prediction crucial.

Purpose of the Study:

• To develop an efficient numerical method for predicting metal plate texture and anisotropy after cold rolling.
• To provide an alternative to experimental methods for anisotropy analysis.

Main Methods:

• Utilized the finite element method (FEM) with zooming analysis.
• Developed an elastic-plastic analysis model for texture prediction.
• Validated the model by comparing simulation results with experimental data.

Main Results:

• Successfully established an efficient elastic-plastic analysis method for cold rolling texture prediction.
• Confirmed the prediction accuracy by simulating the cold rolling of an S45C steel plate.
• Demonstrated the method's effectiveness for body-centered cubic lattice materials.

Conclusions:

• The proposed FEM-based method accurately predicts metal plate anisotropy generated during cold rolling.
• This numerical approach offers a viable alternative to traditional experimental techniques.
• The findings contribute to understanding and managing anisotropy in sheet metal forming.