高压铸造镁合金AZ91延伸率预测分析数据

通过拉伸试验分析高压铸造镁合金AZ91在不同模具充填条件下的力学性能预测值，同时将实验值和预测值进行拟合，获得普适性的延伸率预测模型，用于估算高压铸造镁合金的延伸率。该模型能够根据软件模拟输出，迅速计算出高压铸造镁合金AZ91零件在不同部位的延伸率，从而在设计阶段为材料选择和工艺优化提供科学依据。
"1.数据采集阶段：设计具有不同厚度的高压铸造镁合金AZ91汽车零部件并加工出拉伸样品，利用MTS动态疲劳试验机对样品进行拉伸试验；采集在各种充填条件下材料充填结束时的关键参数，包括温度、流动距离、气体卷入率、空气接触时间和凝固时间等。
2.数据处理阶段：采用多目标优化算法构建预测模型，精确预测材料在不同工艺条件下的力学行为，为材料选择和工艺优化提供数据支持。
AZ91预测值=abs(18.193−15.44∗(F/4)^0.6527−0.5587∗(E/900)^0.6523−6.8476∗(C/35)^4.2−6.3954∗(D/0.02)^0.6635+6.5987∗((B−620)/620)^21.35)，其中F为凝固时间，E为流动距离，C为卷气含量，D为接触空气时间，B为充型完成后温度；
将实验值和预测值进行拟合，获得延伸率预测模型y=αx^β,常数α和β与测试样品的结构相关。
3.应用场景描述：延伸率是评估汽车零部件连接可行性的重要指标，对于在汽车轻量化和模块化设计材料的工程设计至关重要，延伸率预测模型用以估算高压铸造镁合金零AZ91部件在不同位置的材料延伸率。"

This study analyzes the predicted mechanical properties of high-pressure die cast (HPDC) magnesium alloy AZ91 under different mold filling conditions via tensile tests, and fits the experimental and predicted values to obtain a universal elongation prediction model for estimating the elongation of HPDC magnesium alloys. This model can quickly calculate the elongation of AZ91 magnesium alloy parts at different locations based on software simulation outputs, thereby providing scientific evidence for material selection and process optimization during the design phase.

1. Data Collection Stage:
Design automotive components made of HPDC magnesium alloy AZ91 with varying thicknesses, and machine tensile specimens therefrom. Conduct tensile tests on the specimens using an MTS dynamic fatigue testing machine. Collect key parameters at the end of material filling under various filling conditions, including temperature, flow distance, gas entrainment rate, air contact time, and solidification time, etc.

2. Data Processing Stage:
Construct a prediction model using a multi-objective optimization algorithm to accurately predict the mechanical behavior of the material under different process conditions, providing data support for material selection and process optimization.
The predicted value for AZ91 is calculated as:
$ ext{AZ91 predicted value} = left| 18.193 - 15.44 imes left(frac{F}{4}
ight)^{0.6527} - 0.5587 imes left(frac{E}{900}
ight)^{0.6523} - 6.8476 imes left(frac{C}{35}
ight)^{4.2} - 6.3954 imes left(frac{D}{0.02}
ight)^{0.6635} + 6.5987 imes left(frac{B-620}{620}
ight)^{21.35}
ight|$
where F represents solidification time, E represents flow distance, C represents gas entrainment content, D represents air contact time, and B represents the temperature upon completion of mold filling.
Fit the experimental and predicted values to obtain the elongation prediction model $y = alpha x^eta$, where the constants $alpha$ and $eta$ are correlated with the structure of the test specimens.

3. Application Scenario Description:
Elongation is an important indicator for evaluating the connection feasibility of automotive components, and is crucial for the engineering design of materials for automotive lightweighting and modular design. The elongation prediction model is used to estimate the material elongation of AZ91 HPDC components at different positions.

搜集汇总

数据集介绍

特点

该数据集包含512条高压铸造镁合金AZ91的延伸率预测分析数据，通过多目标优化算法构建预测模型，用于估算不同工艺条件下的材料延伸率，为汽车轻量化设计提供科学依据。