This paper investigates the determination of secondary dendrite arm spacing (SDAS) using convolutional neural networks (CNNs). The aim was to build a Deep Learning (DL) model for SDAS prediction that has industrially acceptable prediction accuracy. The model was trained on images of polished samples of high-pressure die-cast alloy EN AC 46000 AlSi9Cu3(Fe), the gravity die cast alloy EN AC 51400 AlMg5(Si) and the alloy cast as ingots EN AC 42000 AlSi7Mg. Color images were converted to grayscale to reduce the number of training parameters. It is shown that a relatively simple CNN structure can predict various SDAS values with very high accuracy, with a $R^2$ value of 91.5%. Additionally, the performance of the model is tested with materials not used during training; gravity die-cast EN AC 42200 AlSi7Mg0.6 alloy and EN AC 43400 AlSi10Mg(Fe) and EN AC 47100 Si12Cu1(Fe) high-pressure die-cast alloys. In this task, CNN performed slightly worse, but still within industrially acceptable standards. Consequently, CNN models can be used to determine SDAS values with industrially acceptable predictive accuracy.

In the present work, the heat generation during the plastic deformation of a multiphase material is studied using machine learning (ML) methods. The aim was to predict the temperature increase from the structure-property relationships (SPR) of a microstructure considering various Taylor–Quinney coefficients (TQCs), with the aim of achieving precision and computational efficiency suitable for industry. Using automatic microstructure generation to create datasets and finite element analysis (FEA) to obtain temperature increase – strain curves, the dataset facilitated the training of an ML model. A 3D convolutional neural network (CNN) was developed using the microstructural configuration and TQC value as input and the temperature increase – strain curve as output. The model demonstrated high prediction accuracy. The results indicated that the hard phase fraction significantly impacts the temperature increase, much more than the TQC values. This underlines the potential of the model for a better understanding of material behavior during deformation and its industrial applicability.