Prediction of Cyclic Stress–Strain Property of Steels by Crystal Plasticity Simulations and Machine Learning

• Published in: Materials Vol. 12; no. 22; p. 3668
• Main Authors: Miyazawa, Yuto, Briffod, Fabien, Shiraiwa, Takayuki, Enoki, Manabu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 07.11.2019; MDPI
• Subjects: artificial neural network; Carbon; Computer simulation; crystal plasticity; cyclic stress-strain curve; Datasets; Electron backscatter diffraction; Experiments; fatigue; Finite element method; Heat treating; Low cycle fatigue; Machine learning; Material properties; Metal fatigue; Microstructure; Morphology; Neural networks; Normal distribution; Pearlite; Plastic properties; Principal components analysis; Probability distribution; Regression models; Simulation; Steel; steels; Strain; Stress-strain relationships; Tessellation; United States > US; Japan
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma12223668

In this study, a method for the prediction of cyclic stress–strain properties of ferrite-pearlite steels was proposed. At first, synthetic microstructures were generated based on an anisotropic tessellation from the results of electron backscatter diffraction (EBSD) analyses. Low-cycle fatigue experiments under strain-controlled conditions were conducted in order to calibrate material property parameters for both an anisotropic crystal plasticity and an isotropic J2 model. Numerical finite element simulations were conducted using these synthetic microstructures and material properties based on experimental results, and cyclic stress-strain properties were calculated. Then, two-point correlations of synthetic microstructures were calculated to quantify the microstructures. The microstructure-property dataset was obtained by associating a two-point correlation and calculated cyclic stress-strain property. Machine learning, such as a linear regression model and neural network, was conducted using the dataset. Finally, cyclic stress-strain properties were predicted from the result of EBSD analysis using the obtained machine learning model and were compared with the results of the low-cycle fatigue experiments.