Efficient failure-free foundry production

Microshrinkages are known as probably the most difficult defects to avoid in high-precission foundry. Depending on the magnitude of this defect, the piece in which it appears must be rejected with the subsequent cost increment. Modelling this environment as a probabilistic constellation of interrelated variables allows Bayesian networks to infer causal relationships. In other words, they may guess the value of a variable (for instance, the presence or not of a defect). Against this background, we present here the first microshrinkage prediction system that, upon the basis of a Bayesian network, is able to foresee the apparition of this defect and to determine whether the piece is still acceptable or not. Further, after testing this system in a real foundry, we discuss the obtained results and present a risk-level-based production methodology that increases the rate of valid manufactured pieces.