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Physical Modelling and Numerical Simulation of the Deep Drawing Process of a Box-Shaped Product Focused on Material Limits Determination

机译:专注于材料限制的箱形产品深拉伸过程的物理建模与数值模拟

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摘要

Similitude theory helps engineers and scientists to accurately predict the behaviors of real systems through the application of scaling laws to the experimental results of a scale model related to the real system by similarity conditions. The theory was applied when studying the deep drawing process of a bathtub made from cold rolled low carbon aluminum-killed steel from the point of view of material limits. The bathtub model was created on the basis of geometric, physical, and mechanical similarity on a scale of 1:5. Thus, simulations and physical models were created. The simulation model was used to verify the combination yield locus/hardening law on the basis of comparing the thickness change. As a result, Hill 48/Krupkowski showed the minimal deviation by comparing data evaluated from numerical simulations and that measured on the physical model. Additionally, material anisotropy was modelled when virtual materials were defined from experimentally measured values of the plastic strain ratio. As an outcome, extra deep drawing quality steel with an average plastic strain ratio of rm ≥ 1.47 and an average strain hardening exponent of nm ≥ 0.23 must be used for the deep drawing of the bathtub.
机译:同意理论有助于工程师和科学家通过将缩放法律应用于与相似性条件相关的规模模型的实验结果来准确地预测真实系统的行为。从材料限制的角度研究了从冷轧低碳铝灭火钢制成的浴缸的深层拉伸过程时应用了该理论。浴缸模型是在1:5的等距的几何,物理和机械相似性的基础上创建的。因此,创建了模拟和物理模型。仿真模型用于在比较厚度变化的基础上验证组合产量轨迹/硬化法。结果,山48 / krupkowski通过比较了从数值模拟评估的数据并在物理模型上测量来显示最小的偏差。另外,当从实验测量的塑性应变比的实验测量值定义虚拟材料时,将材料各向异性建模。作为结果,额外的深层拉伸质量钢,平均塑性应变比率为Rm≥1.47,必须使用NM≥0.23的平均应变硬化指数,必须用于浴缸的深图。

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