Prediction of Mechanical Properties of Biodegradable Zinc Alloys Based on Machine Learning

doi:10.11900/0412.1961.2024.00332

Acta Metall Sin

2026, Vol. 62

Issue (9): 1615-1626 DOI: 10.11900/0412.1961.2024.00332

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Prediction of Mechanical Properties of Biodegradable Zinc Alloys Based on Machine Learning

GUO Chuanping¹, SHI Chenchen¹, LIU Peng¹, GAO Dongfang², ZHAO Yangyang³, QIAO Yang¹(

)

1 School of Mechanical Engineering, University of Jinan, Jinan 250022, China
2 Institute of Medical Sciences, The Second Qilu Hospital of Shandong University, Jinan 250033, China
3 Trauma Orthopedics, Second Hospital of Shandong University, Jinan 250031, China

Cite this article:

GUO Chuanping, SHI Chenchen, LIU Peng, GAO Dongfang, ZHAO Yangyang, QIAO Yang. Prediction of Mechanical Properties of Biodegradable Zinc Alloys Based on Machine Learning. Acta Metall Sin, 2026, 62(9): 1615-1626.

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Abstract

Recent studies indicate that zinc alloys are preferred in biodegradable metal materials owing to their unique biodegradability and biocompatibility. However, their mechanical properties are relatively insufficient; thus, it is crucial to design zinc alloys that meet the mechanical performance implantation standards required for application in biomedicine. The traditional alloy design method depends on experience and trial and error resulting in low efficiency and high cost. In recent years, the rapid development of artificial intelligence has provided new tools and methods for material science. Machine learning (ML), a subset of artificial intelligence, offers new ideas for material design and prediction. This study obtained the mechanical property data of the Zn-Mg-Mn alloy through experimental investigation and literature review. A performance-oriented ML model was used to predict the compressive yield strength (CYS) and hardness of the Zn-Mg-Mn alloy, considering various element types and contents and different alloy preparation processes. Furthermore, the influence of the element types and contents on the microstructure and macroscopic mechanical properties of the material was explored. Based on the existing dataset, a comparative evaluation of seven machine learning algorithms was conducted, and the k-nearest neighbors (KNN) algorithm exhibited the highest predictive performance. To further validate the accuracy of the model prediction, a random method was used to select data beyond the dataset for comparative analysis with the model results. Simultaneously, the Shapley additive explanation method was applied to quantitatively examine the correlation between the two alloying elements, the preparation process, the CYS, and the hardness in the Zn-Mg-Mn alloy. The Mg element was determined to significantly impact the alloy's CYS and hardness. Finally, the influence of individual elements on the mechanical properties of the materials was analyzed through microstructure characterization. The results showed that the formation of new phases (Mg₂Zn₁₁ and MgZn₁₃) due to adding alloy elements significantly affected the mechanical properties. Based on the research results, this study proposed a composition ratio range for the Zn-Mg-Mn alloy to satisfy the mechanical performance standards required for medical implant materials. When Mg content is between 2.25% and 2.50% (mass fraction, the same below) and Mn content is between 2.50% and 3.50%, the CYS and hardness of the alloy comply with the implant standards.

Key words: machine learning zinc alloy biodegradable powder metallurgy mechanical property

Received: 27 September 2024

ZTFLH:

TG146.2

Fund: National Natural Science Foundation of China(52175408);Natural Science Foundation of Shan-dong Province(ZR2023ME077);Natural Science Foundation of Shan-dong Province(ZR2023MC140)

Corresponding Authors: QIAO Yang, associate professor, Tel: 13791051675, E-mail: me_qiaoy@ujn.edu.cn

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	GUO Chuanping
	SHI Chenchen
	LIU Peng
	GAO Dongfang
	ZHAO Yangyang
	QIAO Yang

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https://www.ams.org.cn/EN/10.11900/0412.1961.2024.00332 OR https://www.ams.org.cn/EN/Y2026/V62/I9/1615

Table 1 Powder parameters for the preparation of Zn-Mg-Mn alloy

Fig.1 DSC curves (a, b) and sintering process curve (c) of mixed powders
(a) Zn-Mg hybrid powder (b, c) Zn-Mg-Mn hybrid powder

Table 2 Ranges of input and output variables

Fig.2 Flowcharts for data extension using mean and standard deviation methods (IP and OP are matrices of input and output variables, respectively; i, j, and k are the number of data points, input variables, and output variables, respectively; SD is standard deviation)

Fig.3 Compressive yield strengths of Zn-Mg-Mn alloy predicted by seven algorithms and corresponding experimental values
(a) random forest (RF) (b) logistic regression (LR)
(c) k-nearest neighbors (KNN) (d) decision tree (DT)
(e) extreme gradient boosting (XGBoost) (f) deep neural network (DNN)
(g) convolutional neural network (CNN)

Fig.4 Hardnesses of Zn-Mg-Mn alloy predicted by seven algorithms and corresponding experimental values
(a) RF (b) LR (c) KNN (d) DT (e) XGBoost (f) DNN (g) CNN

Fig.5 Comparisons of coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) for the seven algorithms

Table 3 Comparisons of prediction results of validation sets by KNN algorithm and experimental values^[19,26,27]

Fig.6 Comparisons of predicted values by KNN algorithm and experimental values
(a) CYS (b) hardness

Fig.7 Heat map (a) and statistical chart (b) of feature importance assessments

Fig.8 Analysis of the comprehensive importance of eigenvalues on compressive yield strength and hardness (SHAP—SHapley additive explanations)

Fig.9 Predicted results of effects of alloying element content on CYS (a) and hardness (b)

Fig.10 Mechanical properties (a), XRD patterns (b), and OM (c1, c2) and SEM (c3, c4) images of Zn-3.5Mn-xMg alloys (Red circles in Figs.10c2-c4 represent Mg₂Zn₁₁ phases)
(c1) Mg = 0 (c2) Mg = 1.0% (c3) Mg = 1.5% (c4) Mg = 2.5%

Fig.11 Mechanical properties (a), XRD patterns (b), and SEM images (c1-c4) of Zn-2.5Mg-xMn alloys (Red circles in Figs.11c2-c4 represent MgZn₁₃ phases)
(c1) Mn = 0 (c2) Mn = 1.0% (c3) Mn = 2.5% (c4) Mn = 3.5%

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