A knowledge acquisition model for selecting coordinate measuring machines using inductive learning

The objective of this study is to use the knowledge acquisition model of inductive learning to establish a selection system for coordinate measuring machines. In this paper, an example knowledge base for the selection of a coordinate measuring machine was induced and analyzed from the diffusive information in the example. The goal is to reduce the amount of knowledge related to the event so as to represent the knowledge in the knowledge base with the fewest number of rules possible. Then the concise decision tree is established by means of the knowledge acquisition model to describe the entire selection problem of coordinate measuring machines.