Denoising and feature extraction of weld seam profiles by stacked denoising autoencoder

Abstract

Active vision sensing is widely used in intelligent robotic welding for bead detection and tracking. Disturbed by welding noise such as arc light and spatter, it is a hard work to extract the laser stripe and feature values. This paper presents a method for denoising and feature extraction of weld seam profiles with strong welding noise in gas metal arc welding (GMAW) process by using stacked denoising autoencoder (SDAE). This algorithm encodes the images of various butt joints with strong welding noise to several useful intermediate representations, which can be decoded to the image of pure laser stripe in 1-pixel width. The results show little deviations when there are large spatters across the laser stripe. A back propagation neural network (BPNN) is developed to verify the reliability of the intermediate representations gotten from the encoder, in which the intermediate representations are input neurons and the weld seam width is output neuron. The average width error in training dataset and testing dataset is 0.042 mm and 0.061 mm. The results show that this algorithm can extract the weld seam profiles with strong welding noise and extract feature values accurately.