Microshrinkages are known as probably the most difficult defects to avoid in high-precision foundry. This failure is not corrigible, 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 a microshrinkage appears within a foundry casting. However, this approach needs to label every instance to generate the model that will classify the castings. In this paper, we present a new approach for detecting faulty castings through collective classification to reduce the labelling requirements of completely supervised approaches. Collective classification is a type of semi-supervised learning that optimises the classification of partially-labelled data. We performed an empirical validation demonstrating that the system maintains a high accuracy rate while the labelling efforts are lower than when using supervised learning.