A New Approach of Error Compensation on NC Machining based on Memetic Computation

This paper is a study of the application of Memetic computation integrating and coordinating intelligence algorithms to solve the problems of error compensation for a high-precision numeral control machining system. The primary focus is on development of integrated intelligent computation approach to set up an error compensation system of a numeral control machine tool based on a dynamic feedback neural network. Optimization of error measurement points of a numeral control 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. A dynamic feedback neural network is trained on ant colony algorithm 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.