The CNN architecture used in the current work has been determined after an optimization process explained in the results and discussion section. The optimized CNN architecture is composed of an initial convolution layer using 256 filters and a kernel size of 3. Additionally, a batch normalization technique is added next to the convolution layer to make the output distribution more even, reaching training convergence faster. Then, a max-pooling layer is adopted for a down spatial size sampled of the output, reducing the computational complexity of the network. We use 1D max-pooling with a pooling size and stride size of 1. The second convolution layers follow the same structure and logic explained for the first one. In the third convolution layer a dropout technique is adopted to prevent overfitting and improve generalization during training. The dropout rate is set to 0.2. Before feeding the fully connected layers with the extracted features a flatten layer is used. At the end, two fully connected layers are included obtaining a flatten 1-D array containing the predicted microstructure which is then re-shaped into a 3D matrix (output layer).