Laser Cutting Parameters Optimization Based on Artificial Neural Network

Abstract

In some cases in order to avoid interference during 3D laser cutting of thin metal laser head could not be kept vertical to the surface of a work piece. In such situations the cutting quality depends on not only "typical" cutting parameters but also on the slant angle of the laser head. Traditionally, many tests had to be done in order to obtain best cutting results. In this paper an experimental design is employed to reduce the number of tests and collect experimental training and test sets. An artificial neural network (ANN) approach has been developed to describe quantitatively the relationship between cutting quality and cutting parameters in the non-vertical laser cutting situation. A quality point system is used to evaluate the cutting result of thin sheet quantitatively. The construction of network is also investigated. Testing of this novel method shows that the calculated "quality point" using ANN is quite closely in accord with the actual cutting result. The ANN is very successful for optimizing parameters, predicting cutting results and deducing new cutting information.