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. Modelling the foundry process as an expert knowledge cloud allows machine learning algorithms to foresee the value of a certain variable, in this case, the probability that amicroshrinkage 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 microshrinkages. 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.