监测镁合金近红外和蓝色复合激光焊接中的焊池、锁孔形态和材料渗透状态

IF 3.3 Q2 ENGINEERING, MANUFACTURING
Wei Wei, Yang Liu, Haolin Deng, Zhilin Wei, Tingshuang Wang, Guangxian Li
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引用次数: 0

摘要

由于镁合金对激光的吸收效率较低,导致焊接过程不稳定,焊接质量不达标,这给镁合金的激光焊接带来了挑战。此外,激光焊接过程中信号的复杂性使得准确监测镁合金的熔融状态变得困难。在这项研究中,使用近红外激光和蓝激光对镁合金进行了焊接。通过改变近红外激光器的功率,研究了镁合金对复合激光器的能量吸收模式。采用 U-Net 模型对焊接图像进行分割,以准确提取熔池和锁孔的特征。随后,利用卷积神经网络(CNN)预测穿透状态,并采用局部二进制模式(LBP)特征+反向传播(BP)神经网络的新方法进行比较。提取的图像的 MPA 值和 MIoU 值分别达到 89.54% 和 81.81%,模型的预测准确率高达 100%。讨论了两种监测方法在不同场景中的适用性,为镁焊接质量提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring of the Weld Pool, Keyhole Morphology and Material Penetration State in Near-Infrared and Blue Composite Laser Welding of Magnesium Alloy
The laser welding of magnesium alloys presents challenges attributed to their low laser-absorbing efficiency, resulting in instabilities during the welding process and substandard welding quality. Furthermore, the complexity of signals during laser welding processes makes it difficult to accurately monitor the molten state of magnesium alloys. In this study, magnesium alloys were welded using near-infrared and blue lasers. By varying the power of the near-infrared laser, the energy absorption pattern of magnesium alloys toward the composite laser was investigated. The U-Net model was employed for the segmentation of welding images to accurately extract the features of the melt pool and keyhole. Subsequently, the penetrating states were predicted using the convolutional neural network (CNN), and the novel approach employing Local Binary Pattern (LBP) features + a backpropagation (BP) neural network was applied for comparison. The extracted images achieved MPA and MIoU values of 89.54% and 81.81%, and the prediction accuracy of the model can reach up to 100%. The applicability of the two monitoring approaches in different scenarios was discussed, providing guidance for the quality of magnesium welding.
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来源期刊
Journal of Manufacturing and Materials Processing
Journal of Manufacturing and Materials Processing Engineering-Industrial and Manufacturing Engineering
CiteScore
5.10
自引率
6.20%
发文量
129
审稿时长
11 weeks
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