Dynamic evolution of the weld pool reflection during weld penetration development

Chen, Jinsong; Chen, Jian; Feng, Zhili; Zhang, Yuming

doi:10.1109/aim.2016.7576825

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…熔透控制是实现焊接质量控制和焊接过程自动化的焦点和难点，而实现熔透控制关键在于如何有效地获取表征焊接熔透状态的信号 [1] 。熔池图像包含着最为丰富的信息，能够反映焊接过程的稳定性、焊接熔透状态等情况 [2] 。电弧焊中，一个熟练 20180904 收到初稿，20190520 收到修改稿的焊工通过观察液体熔池表面进行判断，进而调整焊接参数，控制焊接过程达到预定的熔透状态，从而得到合适的焊缝形状和焊接质量 [3][4] 。如果通过视觉检测系统模拟人工操作，通过正面熔池检测预测熔透状态，进而实现闭环控制，具有很大的实用价值 [5] 。人们在将人工神经网络用于实际熔透预测研究中进行了许多探索。肯塔基大学的 KOVACEVIC 等 [6] 基于神经网络建立了熔池尺寸信息与背面熔宽的模型。山东大学高进强 [7] 使用普通 CCD 摄像机拍摄出 GTAW 熔池图像，并提取熔池宽度、熔池半长、熔池后部面积和熔池后拖角等参数，基于神经网络建立正面熔池几何形状参数与背面熔宽的关系模型。刘新锋等 [8][9]…”

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GTAW Penetration Prediction Model Based on Convolution Neural Network Algorithm

Li¹,

Liu²,

Xie³

et al. 2019

Journal of Mechanical Engineering

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：The penetration state can be reflected by the information of the molten pool, but it is difficult to establish a function between the molten pool and the penetration state. To solve this problem, a penetration prediction model based on convolution neural network (CNN) is proposed. Based on the introduction of CNN principle, a molten pool sensing system based on passive vision is designed to collect 2D images of the molten pool. The acquired images are preprocessed to generate the training set and test set for CNN training and testing. Then the prediction mode is built, and the network parameters such as learning rate, batch-size and iterations are optimized. It is found that the model can achieve the best comprehensive performance in prediction accuracy and training time when the size of the first layer convolutional kernel is 99  and the last layer contained 64 convolutional kernels. After training the model with the training set, the trained model is used to predict the penetration state on the test set, and the prediction accuracy is higher than 96%. By visualizing the feature mapping of the prediction model, it is found that the model predicts the penetration state through the features of edge, the position of reflective point and the molten pool tail, thus explaining how the model the judges the penetration state.

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