Building a Machining Knowledge Base for Intelligent Machine Tools

Intelligent machines respond to external environments on the basis of decisions that are made by sensing the changes in the environment and analyzing the obtained information. This study focuses on the construction of a knowledge base which enables decision making with that information. Approximately 70% of all errors that occur in machine tools are caused by thermal errors. In order to proactively deal with these errors, a system which measures the temperatures of each part and predicts and compensates the displacement of each spindle has been developed. The system was built in an open type controller to enable machine tools to measure temperature changes and compensate the displacement. The construction of a machining knowledge base is important for the implementation of intelligent machine tools, and is expected to be applicable to the network-based, intelligent machine tools which look set to appear sooner or later. Key-Words: Machining Knowledge Base, Intelligent Machine Tools, Thermal errors, Offset compensation, Open controller, Multiple linear regression models, Neural Network