Error Compensation on a NC-machine Tool Based on Integrated Intelligent Computation

This paper is a study of the application of integrated intelligent computation to solve the problems of error compensation for high-precision a NC machining system. The primary focus is on the development of integrated intelligent computation approach to get an error compensation system which is a dynamic feedback neural network embedded in a NC machine tool. Optimization of error measurement points of a NC machine tool is realized by way of application of error variable attribute reduction on rough set theory. A principal component analysis is used for data compression and feature extraction to reduce the input dimension of a dynamic feedback neural network and reduce training time of the network. Taking advantage of ant colony algorithm on training of a dynamic feedback neural network does the global search so that network can converge to get a global optimum. Positioning error caused in thermal deformation compensation capabilities were tested using industry standard equipment and procedures. The results obtained shows that this approach can effectively improve compensation precision and real time of error compensation on machine tools.