Automatic Optimization of Focal Point Position in CO_2 Laser Welding with Neural Network in A Focus Control System

摘要

CO_2 lasers are increasingly being utilized for quality welding in production. Considering the high cost of equipment, the start-up time and the set-up time should be minimized. Ideally the parameters should be set up and optimized more or less automatically.In this paper a control system is designed and built to automatically optimize the focal point position, one of the most important parameters in CO_2 laser welding, in order to perform a desired deep/full penetration welding. The control system mainly consists of a multi-axis motion controller - PMAC, a light sensor - Photo Diode, a data acquisition card - DAQCard-700, and a self-learning mechanism - Neural Network.The optimization procedure starts with the welding process being carried out by continuously moving the focal point position from above a welding plate to below the plate, thus the process is ensured to be shifted from initially surface welding to deep/full penetration welding and back to surface welding again. A clear change on plasma brightness from the process is monitored by the photo diode on the front side of the plate with a viewing angle of 45°. The photo diode signal is acquired with the A/D converter card and installed in a computer hard disk for later data processing. Thereafter the optimum focal point position (OFPP) is output by the self-learning mechanism - the neural network. The optimization procedure is completed with the welding process being carried out by adjusting the focus of the laser beam to the OFPP.