Neural network of plume and spatter for monitoring high-power disk laser welding

Abstract

Using multi-characteristic information fusion based on a BP (back propagation) NN (neural network) of the plume and spatters to monitor the high-power disk laser welding of type 304 austenitic stainless steel is presented. An ultraviolet and visible sensitive highspeed video camera was used to capture the dynamic images of laser welding plume and spatters during laser welding. The number and area of spatters, and the area, height, tilt angle and centroid of plume were calculated by using image processing technology and defined as the characteristic parameters of plume and spatters, which were used as inputs of the neural network. The weld bead width was considered as a parameter reflecting the welding status, which was used as output of the neural network. Relations of plume and spatters with laser welding status was established by a BP neural network and experimental results showed that the proposed method could effectively estimate the high-power disk laser welding status when the laser power ranged from 2 kW to 10 kW.