Abstract
The present study proposes a methodology for predicting the mechanical properties of AZ61 and AZ91 alloys associated with microstructure,texture and aging parameters and estimating predictor importance.For this,we investigate quantitative correlations between microstructure,texture and mechanical prop-erties of aged AZ61 and AZ91 rods through machine learning.This regression analysis focuses on the precipitation behavior of Mg17Al12 as the main second phase of Mg-Al-Zn alloys with respect to aging conditions.To simplify data generation,only SEM images were used to quantify the features of discontin-uous and continuous precipitates.To overcome the lack of data and make the most of the measured data,we devised a method to extend the existing dataset by a factor of 9 using the mean and standard devi-ation of the measured data.Artificial neural networks predicted tensile and compressive yield strengths and resultant yield asymmetry with a high accuracy of over 98％using 11 predictors for a total of 288 datasets.Decision tree learning quantitatively assessed the importance of predictors in determining the mechanical properties of aged AZ61 and AZ91 rods.
基金项目
Fundamental Re-search Program(PNK6960)
NETL
NSTL
维普
万方数据