Predicting Mechanical Properties and Corrosion Resistance of Heat-Treated 7N01 Aluminum Alloy by Machine Learning Methods
Xinyu Cao, Yingbo Zhang, Hui Chen
Abstract
Abstract In this paper, mechanical properties and corrosion resistance of different heat-treated 7N01 aluminum alloys are measured. A novel approach is proposed to establish the relationship between heat-treated processing parameters and the properties using machine learning methods. With a set of trustable data, generalized regression neural network (GRNN), support vector machine (SVM) and multiple linear regression (MLR) are applied in the prediction of mechanical properties, while GRNN and SVM, two prevalent machine learning methods, are also employed to predict the corrosion resistance. By comparing coefficient of determination (R 2 ) and mean absolute percentage error (MAPE), we demonstrate, GRNN and SVM are indeed useful machine leaning methods for both modeling and predicting. In virtue of the new approach, we can also establish other goal-oriented models for design and prediction as long as the training data set is available. Finally, it’s found that both single-stage aging and retrogression re-aging can make the alloy reach higher strength values than double-stage aging, however the corrosion resistance of double-stage aging alloy is better than that of single-stage aging.