Proposal of data mining process for tool catalog data introducing machine learning

Abstract

We attempt to construct a novel technology development utilizing big data such as Deep Learning in the manufacturing industry. Especially, we look at the data mining method and the tool catalog as a useful big data base which is updated by tool makers because it is easy for CAD/CAM engineers and machine tool operators to obtain it in the manufacturing fields. In the present report, we proposed the visualization and consideration of cutting condition determination process based on a decision tree method which is one type of statistical analysis method for radius-endmill data base. We also developed a cutting condition prediction system with a random forest which is a type of machine learning method applying a decision tree. Moreover, we performed a case study in endmilling under deriving cutting conditions by the proposed method, which is an unknown and expanded cutting condition based on tool catalog data base. As a result, it is demonstrated that the support based on machine learning is found to be effective to select a cutting condition including an unknown cutting condition in tool catalog data base.

Keywords

• data mining
• decision tree
• random forest
• machine learning
• radius end mill