Possibilities of decision trees applications for improvement of quality and economics of foundry production

The key activity areas, related to quality and economics of foundry production, are presented: designing of manufacturing processes, control of production processes as well as analysis of root causes of process faults and irregularities, are presented. Possibilities of utilization of data mining methods, including decision (classification) trees type learning systems, are indicated. In particular, the role of that kind of tools in decision making concerning selection of process type and optimum materials and parameters as well as in identification of process excessive variations, similarly like with the control charts, are discussed. Evaluation results of classification systems form the viewpoint of their applicability, accuracy and software availability are presented, including decision trees, naïve Bayesian classifier, rough sets theory, direct rule induction methods as well as artificial neural networks. In the final part of the paper a knowledge in the form rules obtained form classification trees is demonstrated using the example of decision making related to application of risers for grey cast iron castings.