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The aim of this study is to develop a new method to predict the effective elastic and thermal behavior of heterogeneous materials using Convolutional Neural Networks CNN. This work consists first of all in building a large database containing microstructures of two phases of heterogeneous material with different shapes (circular, elliptical, square, rectangular), volume fractions of the inclusion (20%, 25%, 30%), and different contrasts between the two phases in term of Young modulus and also thermal conductivity. The contrast expresses the degree of heterogeneity in the heterogeneous material, when the value of C is quite important (C >> 1) or quite low (C << 1), it means that the material is extremely heterogeneous, while C= 1, the material becomes totally homogeneous. In the case of elastic properties, the contrast is expressed as the ratio between Young’s modulus of the inclusion and that of the matrix (C = EiEm), while for thermal properties, this ratio is expressed as a function of the thermal conductivity of both phases (C = λiλm). In our work, the model will be tested on two values of contrast (10 and 100). These microstructures will be used to estimate the elastic and thermal behavior by calculating the effective bulk, shear, and thermal conductivity values using a finite element method. The collected databases will be trained and tested on a deep learning model composed of a first convolutional network capable of extracting features and a second fully connected network that allows, through these parameters, the adjustment of the error between the found output and the expected one. The model was verified using a Mean Absolute Percentage Error (MAPE) loss function. The prediction results were excellent, with a prediction score between 92% and 98%, which justifies the good choice of the model parameters. |
