Application of laser profilo metry in problems of welding equipment geometric adaptation

Abstract

Adaptive adjustment of the relationship between the welding process parameters and the butt geometry permits to reduce the likelihood of welding defects appearance and improve the quality of the welded joint in automatic welding of large-diameter pipes. To obtain data on the configuration of the welded joint, the RF627 laser vision sensor is used. To reduce the influence of restrictions arising during the welding process, a median algorithm for filtering impulse noise is proposed. To calculate the geometric parameters of the welded joint, a model based on pixel data obtained from a laser sensor is proposed. The restoration of the welded butt parameters is carried out according to the algorithm of piecewise-linear approximation, which involves the determination of six characteristic points of the butt. The adaptive adjuster uses an inverse neural network model for adjustment the parameters of the welding process: welding current, voltage, wire feed speed. To train the neural network, the characteristic parameters of the welded butt are used: gap, skewing (warping of the edges) and bluntness (for the root weld), the current width of the butt groove in each layer (for other types of welds). The weights of the neural network layers are restored online using a gradient descent algorithm. The important role of the laser vision sensor in solving the problem of adaptation of welding equipment and the effectiveness of the proposed algorithms are confirmed experimentally. Keywords laser vision sensor; robotic welding; multilayer/multi-pass welding; piecewise linear approximation; adaptive control with a reverse neural network model