Using inductive machine learning to support decision making in machining processes

In spite of their practical success, knowledge-based systems still suffer from considerable limitations. Specialized for problem solving in a narrow domain, most systems possess very limited knowledge and are rather inflexible. Moreover, building a knowledge base is the most critical phase in developing an expert system. In overcoming these limitations, existing machine learning techniques, capable of deriving concepts from data, can be effectively applied. This work focuses on machine learning from examples and its potential in discovering knowledge hidden in technological databases. Practically oriented studies of automating two-decision procedures related to machining processes are presented: classification of dielectric fluids used in electrical discharge machining, and tool selection in an industrial grinding process. The results show the approach is beneficial in preventing poor process performance and improving product quality. It also allows for better understanding of the processes at the shop floor level, and advances decision making at the technology planning level