Enhancing fault prediction on automatic foundry processes

Microshrinkages are known as probably the most difficult defects to avoid in high-precision foundry. This failure renders the casting invalid, with the subsequent cost increment. Mod-elling the foundry process as an expert knowledge cloud allows machine learning algorithms to fore-see the value of a certain variable, in this case, the probability that a microshrinkage appears within a casting. In this paper, we extend previous research on foundry production control by adapting and testing support vector machines and decision trees for the prediction in beforehand of microshrin-kages. Finally, we compare the obtained results and show that decision trees are more suitable than the rest of the counterparts for the prediction of microshrinkages.