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Xu Qin, Qinghang Wang, Li Wang, Shouxin Xia, Haowei Zhai, Lingyu Zhao, Ying Zeng, Yan Song, and Bin Jiang, Interpretable machine learning-based stretch formability prediction of magnesium alloys, Int. J. Miner. Metall. Mater.,(2025). https://doi.org/10.1007/s12613-024-3002-9
Xu Qin, Qinghang Wang, Li Wang, Shouxin Xia, Haowei Zhai, Lingyu Zhao, Ying Zeng, Yan Song, and Bin Jiang, Interpretable machine learning-based stretch formability prediction of magnesium alloys, Int. J. Miner. Metall. Mater.,(2025). https://doi.org/10.1007/s12613-024-3002-9
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基于可解释机器学习的镁合金杯突成形性能预测

摘要: 镁合金因其质轻、比强度高、阻尼减振等优点,具有广阔的应用前景,但其塑性变形能力较差,限制了其广泛应用。AZ31镁合金作为一种典型材料,其拉伸成形性研究对于拓展其应用具有重要意义。本研究旨在开发一个可解释的预测框架,通过结合极端梯度提升(XGBoost)模型和麻雀搜索算法(SSA),评估AZ31镁合金的拉伸成形性,以期为高成形性镁合金的开发提供理论支持和工具。研究从文献和实验中收集数据,提取了11个特征,包括微观结构(如晶粒尺寸、最大极强度、织构分散度等)、力学性能(如拉伸屈服强度、抗拉强度、断裂伸长率等)和测试条件(如板材厚度、冲头速度等)。通过皮尔逊相关系数和穷举筛选方法,确定了10个关键特征作为最终输入。基于这些特征,构建了SSA-XGBoost模型,并采用Erichsen指数(IE)作为模型输出,通过Shapley加性解释方法和XGBoost特征重要性分析,建立了关键特征与IE值之间的定量关系。研究结果表明,SSA-XGBoost模型表现出良好的预测性能,拟合优度(R²)达到0.91,显著优于传统机器学习模型。通过新数据集验证,模型预测的IE值与实验值之间的误差低于5%。研究发现,最大极强度(Imax)、拉伸屈服强度(TYS)、断裂伸长率(EL)、最大极位置的半径(r)、晶粒尺寸(GS)和强度差(∆S)是影响IE值的关键特征。本研究开发的SSA-XGBoost模型为AZ31镁合金拉伸成形性的预测提供了一个可靠且准确的工具,为高成形性镁合金的开发提供了新的思路和方法。

 

Interpretable machine learning-based stretch formability prediction of magnesium alloys

Abstract: This study involved the development of an interpretable prediction framework to access the stretch formability of AZ31 magnesium alloys through the combination of the extreme gradient boosting (XGBoost) model with the sparrow search algorithm (SSA). Eleven features were extracted from the microstructures (e.g., grain size (GS), maximum pole intensity ( I_\mathrmm\mathrma\mathrmx ), degree of texture dispersion (μ), radius of maximum pole position (r), and angle of maximum pole position (A)), mechanical properties (e.g., tensile yield strength (TYS), ultimate tensile strength (UTS), elongation-to-failure (EL), and strength difference (∆S)) and test conditions (e.g., sheet thickness (t) and punch speed (v)) in the data collected from the literature and experiments. Pearson correlation coefficient and exhaustive screening methods identified ten key features (not including UTS) as the final inputs, and they enhanced the prediction accuracy of Index Erichsen (IE), which served as the model’s output. The newly developed SSA-XGBoost model exhibited an improved prediction performance, with a goodness of fit (R2) of 0.91 compared with traditional machine learning models. A new dataset (four samples) was prepared to validate the reliability and generalization capacity of this model, and below 5% errors were observed between predicted and experimental IE values. Based on this result, the quantitative relationship between the key features and IE values was established via Shapley additive explanation method and XGBoost feature importance analysis. I_\mathrmm\mathrma\mathrmx , TYS, EL, r, GS, and ΔS showed a crucial influence on the IE of 10 input features. This work offers a reliable and accurate tool for the prediction of the stretch formability of AZ31 magnesium alloys and provides insights into the development of high-formable magnesium alloys.

 

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