Overcoming process delays with decision tree induction

Abstract

Printers are always seeking higher productivity by increasing their production rates and minimizing process delays. When process delays have known causes, they can be mitigated by acquiring causal rules from human experts and then applying sensors and automated real-time diagnostic devices to the process. However, for some delays the experts have only weak causal knowledge or none at all. In such cases, machine learning tools can collect training data and process it through an induction engine in search of diagnostic knowledge. We have applied a machine learning strategy known as decision tree induction to derive a set of rules about a long-standing problem in rotogravure printing. The induction mechanism is embedded within a knowledge acquisition system that suggests plausible rules to an expert, who can override the rules or modify the data from which the rules were derived. By using decision tree induction to derive process control rules, this system lets experts participate in knowledge acquisition by doing what they do best: exercising their expertise.<>