基于卷积神经网络的CO_2焊接熔池图像状态识别方法

为了通过熔池图像对焊接状态进行判断,将卷积神经网络引入到CO_2焊接熔池图像状态识别中,提出了一种CO_2焊接熔池状态识别卷积神经网络CNN-M。该网络使用简单预处理的熔池图像作为输入向量,避免了人工提取图像特征的主观性对识别率的不良影响。同时,CNN-M采用了ReLU激活函数、随机Dropout及SVM分类器来降低样本集稀少可能导致的网络过拟合现象。试验结果表明,和人工提取熔池特征状态作为输入向量的BP神经网络相比,CNN-M在识别率及识别速度方面均体现出了更好的性能,其良好的泛化能力能够满足在线熔池状态监控的要求。

Recognition of molten pool morphology in CO2 welding based on convolution neural network

A kind of convolution neutral network CNN-M was proposed to recognize the morphology of molten pool in CO2 welding. A simple pretreatment of the molten pool image was adopted as the input vector in CNN-M, which avoids the adverse effects brought by the subjectivity of artificial extraction of molten pool image features. In order to decrease the possibility of network over-fitting caused by the sparse training data set, several methods including ReLU activation function, random Dropout and SVM classifier were used in CNN-M. The experimental results show that CNN-M has better performance in recognition rate and recognition speed than the BP neural network does, which adopt the characteristic values of the molten pool as the input vector. The performance of CNN-M is able to meet the requirement of the on-line molten pool monitoring.