Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network

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.

Advances in Design by Metallic Materials: Synthesis, Characterization, Simulation and Applications

“…The collection includes papers regarding the most multifaced aspects of metals as synthesis [1][2][3], treatments [2][3][4], experimental characterization [4][5][6][7], material models [7][8][9] and engineering applications [10][11][12] providing a clear cross-section of the wide variety of topics and research arguments under investigation in the scientific community now.…”

“…With such a scope, standard mechanical experiments (i.e., tensile tests) were combined with an advanced approach based on pattern recognition and machine learning able to find physical recurrencies where a human eye cannot discover anything. Similar artificial intelligence tools, based on artificial neural networks, were also adopted in other papers such as [7] on the tensile behavior of an austenitic stainless steel or [5] on the determination of hardness and other surface properties.…”

Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

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“…The can accurately measure the deviation between process values and estimated ones, and the is able to provide information on the strength of correlation between them. They are calculated using the following equation: Taking into account that the model will be used for a complex and rigorous process, then it is also convenient to verify the accuracy of the established models for diagnosing the change of stopper rod using goodness-of-fit statistical parameters such as: RMSE, correlation coefficient (R) and the mean absolute percentage error (MAPE) [25,33].…”

“…By means of artificial neural networks the FIS should be trained using data sets, with valve erosion and valve clogging corresponding to steel casts in each case study. Learning from data is also an efficient statistical analysis method to capture the linear or nonlinear internal relationships used for applications of continuous casting [20][21][22][23][24][25].…”