典型文献
Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning
文献摘要：
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.
文献关键词：
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作者姓名：
Joung Sik Suh;Byeong-Chan Suh;Sang Eun Lee;Jun Ho Bae;Byoung Gi Moon
作者机构：
Advanced Metals Division,Korea Institute of Materials Science,Changwon 51508,Republic of Korea
文献出处：
引用格式：
[1]Joung Sik Suh;Byeong-Chan Suh;Sang Eun Lee;Jun Ho Bae;Byoung Gi Moon-.Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning)[J].材料科学技术（英文版）,2022(12):52-63
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0.530668
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