不锈钢激光焊接头超声导波检测与质量评价技术

赵雪山; 段珍珍; 周广浩; 郑月生; 谷晓鹏

doi:10.7512/j.issn.1001-2303.2024.05.21

无损检测与质量保证 | 浏览量 : 0 下载量: 961 CSCD: 0

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不锈钢激光焊接头超声导波检测与质量评价技术
Research on Ultrasonic Guided Wave Detection and Quality Evaluation Technology for Stainless Steel Laser Welding Head
2024年54卷第5期页码：142-147
收稿：2023-12-21，

修回：2024-02-21，

纸质出版：2024-05-25
DOI： 10.7512/j.issn.1001-2303.2024.05.21
稿件说明：

移动端阅览

赵雪山，段珍珍，周广浩，等.不锈钢激光焊接头超声导波检测与质量评价技术［J］.电焊机，2024，54（5）：142-147. DOI： 10.7512/j.issn.1001-2303.2024.05.21.

ZHAO Xueshan, DUAN Zhenzhen, ZHOU Guanghao, et al.Research on Ultrasonic Guided Wave Detection and Quality Evaluation Technology for Stainless Steel Laser Welding Head[J].Electric Welding Machine, 2024, 54(5): 142-147. DOI： 10.7512/j.issn.1001-2303.2024.05.21.

摘要

半熔透型搭接激光焊工艺质量控制难度大，如何有效保证板层间的熔合宽度是开发和应用半熔透型搭接激光焊技术的关键。为此，开展薄板焊接接头超声导波检测方法研究，研发接头内部熔合宽度检测与评估系统，通过对超声导波信号时、频域特征分析，确定能够表现接头内部熔合状态的信号特征值，并以此建立能够有效区分不同焊接质量的评价模型，从而实现不锈钢半熔透型搭接激光焊接头内部熔宽的在线检测。研究结果表明，以超声导波时域信号上最大归一化幅值、频域上第一波峰和第二波峰的峰值为输入信息，采用BP神经网络算法进行分类预测，能够有效识别未熔合、熔合宽度不足、熔合宽度合格等连接状态，预测准确率可达94.4%。

Abstract

The laser welding process with a semi-penetration lap joint

due to its advantages of high efficiency and aesthetics

is currently highly valued as a manufacturing technology for the new generation of stainless steel subway body. However

the quality control of this welding process is difficult

and how to effectively ensure the fusion width between the layers is the key to developing and applying laser welding technology with a semi-penetration lap joint. To this end

research was conducted on the ultrasonic guided wave detection method for thin plate welded joints

and a system for detecting and evaluating the internal fusion width of joints was developed. By analyzing the time-frequency characteristics of ultrasonic guided wave signals

the signal characteristic values that can reflect the internal fusion state of joints were determined. Based on this

an evaluation model that can effectively distinguish different welding qualities was established

thus achieving online detection of the internal fusion width of semi-penetration laser welded lap joints for stainless steel. The research results show that using the maximum normalized amplitude of the ultrasonic guided wave time-domain signal and the peak values of the first and second peaks in the frequency domain as input information

and using the BP neural network algorithm for classification prediction can effectively identify different connection states such as incomplete fusion

insufficient fusion width

and qualified fusion width

with a prediction accuracy of 94.4%.

关键词

Keywords

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