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In this work, a model has been developed to determine thermal and microstructural events during non-isothermal annealing of rolled carbon steels. In the first place, the process of cold rolling under both symmetrical and asymmetrical conditions was mathematically modeled employing an elastic-plastic finite element formulation to define the distribution of plastic strain and internal stored energy. In the next step, two-dimensional model based on cellular automata was generated to assess softening kinetics in annealing treatment. At the same time, a thermal model based on Galerkin-finite element analysis was coupled with the microstructural model to consider temperature variations during heat treatment. The impact of different parameters such as heating rate, annealing temperature, and initial microstructures were all taken into account. To validate the employed algorithm, the predictions were compared with the experimental results and a reasonable agreement was found. Accordingly, the simulation results can be employed for designing a proper mechanical-thermal treatment to achieve the desired microstructure as well as mechanical properties under practical processing conditions.

Fatigue damage process inherently has multiscale characteristics. As a result, fatigue cracks mainly classified as short cracks (SCs) and long cracks (LCs). It is necessary to quantify the fatigue crack growth (FCG) rate in both the short and long crack regimes. Especially in the case of lightweight alloys and high cycle fatigue in which short cracks' behavior dominates total fatigue life. There is still no proper model to characterize FCG rate in the SC regime. In the presented study, a radial basis function artificial neural network (RBF-ANN) model as a machine learning approach has been developed to quantify the FCG rate in both the SC and LC regimes. Experimental data sets of 2024-T3 and 7075-T6 aluminum alloys are employed to train and verify the model. The presented study showed that the RBF-ANN model can accurately predict the nonlinearity of FCG rate in terms of stress intensity factor range in both the SC and LC regime. However, the predictions showed that the extrapolation ability of the model is not as appropriate as its interpolation capability. In addition, density and distribution of the input data strongly affect the accuracy of the RBF-ANN model.

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