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 (Imax), 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. Imax, 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.

1. Introduction

Magnesium (Mg) and its alloys are the most promising lightweight metallic materials with broad application prospects in the aerospace, automotive, and medical industries [1]. However, these materials frequently exhibit poor stretch formability at room temperature, which considerably limits their large-scale applications due to their low-symmetry hexagonal close-packed crystal structure [2]. A 2–3 mm Index Erichsen (IE), which is considerably lower than that beyond 15 mm in some aluminum (Al) alloys, was obtained in a strong basal-textured AZ31 alloy sheet [3–5]. Features of materials, particularly microstructural features, e.g., texture and grain size (GS), influence stretch formability. Texture weakening is generally an effective method for the activation of more basal slip systems, and it provides a large strain in the thickness direction [6–8]. The influence of GS on the formability of Mg alloys is a complex and unpredictable phenomenon. Kang et al. [9] and Park and Shin [10] pointed out that large GS is beneficial for the enhancement of formability through the increase in the work hardening capacity. This finding was mainly due to the activation of {1012} tensile twins in large grains during the Erichsen cupping test. However, Wei et al. [11] argued that grain refinement can activate more nonsubstrate slip systems, which improves formability.

Machine learning techniques offer a powerful means of data analysis and enable the identification of potential influencing factors and patterns through the mining of large volumes of experimental data. Nevertheless, thus far, reports on the accurate prediction of stretch formability of Mg alloys are limited, and quantitative analysis of the factors affecting them remains lacking. Machine learning algorithms, e.g., extreme gradient boosting (XGBoost), artificial neural network (ANN), long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and regression tree (RT), have been used in the accurate prediction of the mechanical and corrosion properties of Mg alloys and establishment of the correlations between them and microstructures [12–18]. Zhang et al. [15] investigated the relationship between the texture and tensile properties of AZ31 Mg alloys using the ANN model and observed ...