Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features
Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features
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DOI：
10.1016/j.eswa.2023.121425
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发表时间：
2023-09-09
影响因子：
8.5
通讯作者：
Wu，Chuansong
中科院分区：
文献类型：
--
作者：
Zhou，Fangzheng;Liu，Xinfeng;Wu，Chuansong
锁孔等离子弧焊虽然效率很高，但也存在焊接参数范围窄、焊接过程容易受到干扰、焊接质量不稳定等缺点。准确预测焊孔/焊透状态是保持焊接过程稳定性和提高焊接质量的重要前提。大多数研究人员都集中在视觉检测技术和卷积神经网络（CNN）上，建立了焊接池图像与焊透状态之间相关性的数学模型。虽然CNN可以提取单个熔池图像的特征，但很难预测入射矩的演变趋势。本文建立了一种基于CNN和LSTM（长短期记忆）的新型模型，用于提取上部甲板焊接池图像的时空特征，从而预测和描述复杂的锁孔行为。分别对单个CNN模型、单个LSTM模型以及将单个图像的空间特征与序列的时间特征相结合的CNN-LSTM模型进行对比研究。对全熔透焊接中典型的关键焊接场景——锁孔初始化和建立阶段进行了研究和比较，得出了与实际情况较为接近的预测值。此外，采用提前预测锁孔状态，即使在预测未来2 s的锁孔行为时，也能保持80%以上的精度。因此，统一的CNN-LSTM模型有效地提高了锁孔/穿透状态的预测精度，为智能K-PAW技术提供了前景。
Despite the high efficiency of keyhole plasma arc welding (K-PAW), it still has several deficiencies, such as narrow welding parameter ranges, easily disturbed welding process and instability of welding quality, etc. It is a significant prerequisite to predict the keyhole/penetration status accurately for maintaining the welding process stability and improving the welding quality. Most researchers have focused on visual inspection techniques and convolutional neural networks (CNN), establishing a mathematical model of the correlation between weld pool images and penetration status. While CNN could extract the features of single weld pool images, it is difficult to predict the evolution trend of the incoming moments. In this paper, a novel model based on CNN and LSTM (long-short term memory) was developed to extract both the spatial and temporal features of topside weld pool images, and consequently the complex keyhole behaviors were predicted and described. The comparative study is carried out on different models, i.e. the single CNN model, the single LSTM model, and the CNN-LSTM model that integrates both spatial features of single images and temporal features of sequence. The keyhole initialization and establishing period, as the typical and critical welding scenario in full-penetration welding, is investigate and compared, resulting in a predicted value that is close to reality. Furthermore, ahead prediction of keyhole status was adopted, maintaining over 80% accuracy even when predicting keyhole behaviors 2 s into the future. Consequently, the unified CNN-LSTM model effectively improves the prediction accuracy of keyhole/penetration status, promising for intelligent K-PAW technology.