Optimización de parámetros de CNC de acuerdo a criterios de productividad usando un modelo de máquina basado en redes neuronales

Abstract

Every machine-tool user wants to maximize the productivity

of their machines looking for balance between speed,

precision and lifetime of mechanical components.

Nevertheless, because CNCs have wide-ranging use, their

correct parametrization for each case is key to achieving

the desired objectives; on the other hand, minimizing the

numbers of experimental tests to be performed on the

machine is essential to reduce time and costs of the set-up

process. In order to solve both difficulties, this paper presents

a tool to give final user necessary information to properly

adjust CNC parameters according to productivity criteria. The

method makes use of experimental data to obtain a model

of the machine based on neural networks. With this model

machining time, geometric error and smoothness of any

piece to be manufactured can be predicted, and therefore

minimizing test on the real machine and recommending the

appropriate values for the CNC.