Abstract
Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.
Highlights
Metallic materials are widely used in daily life, especially a variety of steel that have a very long history of research
We proposed a machine learning method using artificial neural network (ANN) to predict the tensile properties of austenitic stainless steel (ASS) with the chemical composition, solution treatment conditions and test temperature as descriptors
In order to verify the accuracy of the established models, besides the root mean square error (RMSE) and R, another goodness-of-fit statistical parameter is the mean absolute percentage error (MAPE) [26], which is used goodness-of-fit statistical parameter is the mean absolute percentage error (MAPE) [26], which is used to measure the error between the predicted and original values, and it considers the ratio to measure the error between the predicted and original values, and it considers the ratio of error to the original values
Summary
Metallic materials are widely used in daily life, especially a variety of steel that have a very long history of research. MatNavi contains a large amount of data about the fatigue and creep properties of various steels, which have been already used for machine learning model establishment and research. Besides the fatigue and creep properties, a few machine learning models have been established to obtain the correlations between the tensile properties of steels and the important variables. There are no general machine learning models to correlate chemical composition, heat processes and service conditions to tensile properties of ASS, and clarify how each variable affects the tensile properties of ASS. We proposed a machine learning method using ANN to predict the tensile properties of ASS with the chemical composition, solution treatment conditions (heat processes) and test temperature (service condition) as descriptors. Our results conform to the previous metallurgical theories, and the established models can guide us for further research and development of new ASS with the expected tensile properties
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