Error Compensation in a Horizontal Machining Center Using Artificial Intelligence Strategy

This paper investigates the application of artificial neural networks to the problem of calculating error compensation values for axis motion on a horizontal machining center. Firstly, traditional compensation methods and artificial intelligence strategies are introduced. Secondly, some multilayer neural network architectures are examined for applicability to the problem. Using standard modeling techniques, the motion error compensation model for a horizontal machining center is developed. And this model is parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. Relevant programs are developed in Matlab development environment, and modified termination algorithms are applied to reduce computation times. Finally, experiments are carried out to validate the approaches proposed in this paper. The results show that artificial neural networks are capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce motion errors.