Machine-learning-based mechanical properties prediction in foundry production

Ultimate tensile strength (UTS) is the force a material can resist until it breaks. The only way to examine this mechanical property is the employment of destructive inspections with the subsequent cost increment. Modelling the foundry process as an expert knowledge cloud allows properly-trained machine-learning algorithms to foresee the value of UTS. Extending previous research that presented outstanding results with a Bayesian-network-based approach, we have adapted an ANN and K-nearest-neighbour algorithm for the same objective. We compare the obtained results and show that artificial neural networks are more suitable than the rest of counterparts for the prediction of UTS.