In-process modelling and estimation of thermally induced errors of a machine tool during cutting

Abstract

A compact measurement system was developed to measure the time variant machine tool errors during cutting. The system is composed of a gauge, a sensor holder 5 gap-sensors and a PC. The gauge is made of invar which has a very low thermal expansion coefficient, and is often used to measure the thermally induced errors of a machine tool. A new neural network model was considered to estimate the time variant machine tool errors during cutting using a new concept of input values. The detail of the model proposed is described in the paper together with experimental methodologies using a compact measurement system to examine the validity of this approach. These schemes were implemented oil a small vertical-machining centre.