基于焊接图像表征学习的焊点熔透状态顺序识别算法

IF 6.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
Xudong Zhang , Fei Wang , Yourong Chen , Heng Zhang , Liyuan Liu , Qiyue Wang
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引用次数: 0

摘要

焊点的熔透状态决定了焊缝的质量,但它受到空间的限制,通常难以直接测量。通常使用现有的焊接工艺信息对其进行估计。本研究提出了一种基于焊缝图像表征学习的焊缝熔透状态序列识别算法(WJPSSI),并在以下两个方面做出了贡献:(1)在无标签条件下,利用自监督表征学习方法从焊池图像中提取特征,解决了工业生产中标签获取的难题;(2)建立了几发定时学习方法,确定熔透状态演变过程中的定时相关性。通过建立以脉冲气体钨极氩弧焊(GTAW-P)为应用重点的双摄像头视觉传感系统,完成了焊接图像和焊点熔透状态的同步采集,并创建了不同焊接条件下熔化时间-背面焊池宽度的序列数据集。本研究利用基于通道注意机制的自动编码器模型(New-AutoEncoder,NAE)对焊池图像进行自监督表示和特征提取。此外,研究还引入了一种自注意机制,利用注意门控递归单元(AGRU)开发了一种改进的背面焊池宽度监测模型,用于及时预测背面焊池宽度和确定熔透状态。通过模型结构优化、参数训练和性能验证,两组的时间序列预测精度均方根误差为 0.29 mm,与其他预测方法相比有显著提高。实验结果表明,所提出的方法为焊接接头的少次序列识别树立了新的标杆。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weld joint penetration state sequential identification algorithm based on representation learning of weld images

The penetration state of weld joints determines the quality of the weld, but it is limited by space and is often difficult to measure directly. It is typically estimated using available weld process information. This study proposes a Weld Joint Penetration State Sequential Identification algorithm based on the representation learning of weld images (WJPSSI) and makes contributions in the following two aspects: (1) Features are extracted from weld pool images using a self-supervised representation learning method under label-free conditions, addressing the challenge of label acquisition in industrial production; (2) A few-shot timing learning method is established to determine the timing correlation in the evolution process of the penetration state. By establishing a dual-camera visual sensing system with Pulsed Gas Tungsten Arc Welding (GTAW-P) as the application focus, the synchronous collection of weld images and the penetration states of weld joints is completed, and a dataset of sequential melting time — backside weld pool width under various weld conditions is created. This study utilizes an autoencoder model (New-AutoEncoder, NAE) based on the channel attention mechanism for self-supervised representation and feature extraction of the weld pool image. Additionally, a self-attention mechanism is introduced to develop an improved backside weld pool width monitoring model using the Attention-Gated Recurrent Unit (AGRU) for timely prediction of backside weld pool width and penetration state determination. Through model structure optimization, parameter training, and performance verification, the time-series prediction accuracy achieved an RMSE of 0.29 mm across two groups, marking a significant improvement over other prediction methods. Experimental results show that the proposed method sets a new benchmark in the few-shot sequential identification of weld joints.

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来源期刊
Journal of Manufacturing Processes
Journal of Manufacturing Processes ENGINEERING, MANUFACTURING-
CiteScore
10.20
自引率
11.30%
发文量
833
审稿时长
50 days
期刊介绍: The aim of the Journal of Manufacturing Processes (JMP) is to exchange current and future directions of manufacturing processes research, development and implementation, and to publish archival scholarly literature with a view to advancing state-of-the-art manufacturing processes and encouraging innovation for developing new and efficient processes. The journal will also publish from other research communities for rapid communication of innovative new concepts. Special-topic issues on emerging technologies and invited papers will also be published.
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