A Knowledge Transfer Framework for General Alloy Materials Properties Prediction.

Biomedical metal implants have many applications in clinical treatment. Due to a variety of application requirements, alloy materials with specific properties are being designed continuously. The traditional alloy properties testing experiment is faced with high-cost and time-consuming challenges. Machine learning can accurately predict the properties of materials at a lower cost. However, the predicted performance is limited by the material dataset. We propose a calculation framework of alloy properties based on knowledge transfer. The purpose of the framework is to improve the prediction performance of machine learning models on material datasets. In addition to assembling the experiment dataset, the simulation dataset is also generated manually in the proposed framework. Domain knowledge is extracted from the simulation data and transferred to help train experiment data by the framework. The high accuracy of the simulation data (above 0.9) shows that the framework can effectively extract domain knowledge. With domain knowledge, the prediction performance of experimental data can reach more than 0.8. And it is 10% higher than the traditional machine learning method. The explanatory ability of the model is enhanced with the help of domain knowledge. In addition, five tasks are applied to show the framework is a general method. [ABSTRACT FROM AUTHOR]