Visual prediction model for weld seam offset based on deep learning

Abstract

To test the weld penetration situation, a new method based on deep learning has been researched. The welding technique in paper is TIG weld. The weld testing and experiment system has been firstly setup. Weld experiments can be performed. A CCD sensor is used to snap images during the welding procession. A neural filter and a narrow band filer are chosen to setup a composite system, which can filter weld arc light. The experiment system can capture several groups of weld pool images in real time. The pool images are processed by median filter and gray transformation operations. On this basis, the CNN(convolution neural network) is built up. The input layer contains 215×215 neurodes. The character extraction network is a single convolution layer composed by 20 convolutional filters, which are 9×9. The active function of convolution layer is the ReLU function. The 2×2 average pooling method is used for the pool layer. And the classifier network contains one hidden and a output layer. There are 100 neurodes in hidden layer, and the activated function of it is the ReLU function. The neurodes number in output layer is three, which stand for the unfused, fused and overfused condition of weldments. The Soft max function is determined to be activated function for output layer. Then 300 pool images are used to be sample data, which are captured by welding experiments. And the set up neural network can be trained by the data. So a visual CNN model can be setup for weld penetration predicting. In the end of the paper, experiment is performed to test the accuracy. Another 100 pool images are imported to the setup CNN model. The precision is up to 92%, which is showed that the setup network has certain accuracy rate.