Predictionof mechanical properties of zinc alloy based on machine learning algorithm

As a medical implant material, zinc alloys need to have high strength and sufficient hardness to support bone regeneration. Therefore, it is important to clearly define the design criteria for zinc alloys that meet the mechanical property requirements of degradable medical implants. In this work, mechanical property data of Zn-Mg-Mn alloys were obtained through experimental research and literature collection. A performance-oriented machine learning (ML) model was used to predict the compressive yield strength and hardness of Zn-Mg-Mn alloys with different element types, contents and alloy preparation processes, and then the influence of element types and contents on the microstructure and macroscopic mechanical properties of the material was explored. Based on the existing dataset, the model compared six different ML algorithms and identified the optimal prediction algorithm. To further verify the accuracy of the model’s predictions, data outside the dataset were randomly selected for comparative analysis with the model results. The results showed that the prediction errors of the algorithm designed in this work for compressive yield strength and hardness were less than 2 % and 2.4 %, respectively.