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工程设计学报
Chin J Eng Design  2023, Vol. 30 Issue (5): 562-570    DOI: 10.3785/j.issn.1006-754X.2023.00.066
Theory and Method of Mechanical Design     
Research on detection method of underwater welding quality based on acoustic signal recognition
Xiaodong JI(),Tianyu CHENG(),Liang HUA,Xinsong ZHANG
College of Electrical Engineering, Nantong University, Nantong 226000, China
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Abstract  

Underwater welding is widely used in many fields, but its welding quality is difficult to guarantee. Aiming at the problems of high nonlinearity, strong parameter coupling and low detection efficiency in underwater welding process, a new method for underwater welding quality detection based on acoustic signal recognition was proposed. The method constructed a monitoring system based on acoustic signal acquisition underwater to collect the acoustic information during the welding process of weldments in real time, and built a double-weight neural network (DWNN) model through filtering and noise reduction processing and feature extraction for the acoustic signal. The model had excellent nonlinear fitting ability of high-dimensional data and could realize nonlinear mapping between multi-parameters of underwater welding and multi-features of acoustic signals, and it could still realize high-precision pattern recognition in the case of small samples. The underwater welding quality detection experiments were carried out with high strength and low carbon alloy steel—HSLA-115 steel as welding object. The results showed that the recognition accuracy of DWNN model applied to underwater welding quality detection could reach 100%. The research results can provide reference for the optimization of underwater welding process and the construction of underwater weldparts expert knowledge base.

Key words： underwater welding      acoustic signal      feature extraction      double-weight neural network     
Received: 02 March 2023      Published: 03 November 2023
CLC:  TG 456.5  
Corresponding Authors: Tianyu CHENG     E-mail: 1731571044@qq.com;chengtianyu518@163.com
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Xiaodong JI
Tianyu CHENG
Liang HUA
Xinsong ZHANG
Cite this article:

Xiaodong JI,Tianyu CHENG,Liang HUA,Xinsong ZHANG. Research on detection method of underwater welding quality based on acoustic signal recognition. Chin J Eng Design, 2023, 30(5): 562-570.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2023.00.066     OR     https://www.zjujournals.com/gcsjxb/Y2023/V30/I5/562


基于声信号识别的水下焊接质量检测方法研究

水下焊接的应用领域广泛，但其焊接质量难以保障。针对水下焊接处理过程中存在的非线性程度高、参数耦合性强以及检测效率低等问题，提出了一种新的基于声信号识别的水下焊接质量检测方法。该方法通过在水下构建基于声信号采集的监测系统，实时采集焊件焊接过程中的声信息，并通过对声信号进行滤波降噪处理和特征提取，构建双权值神经网络（double-weight neural network, DWNN）模型。该模型具有优秀的高维数据非线性拟合能力，可实现水下焊接多参数与声信号多特征之间的非线性映射，且在小样本情况下仍能实现高精度的模式识别。以高强度低碳合金钢——HSLA-115钢作为焊接对象，开展水下焊接质量检测实验。结果表明，DWNN模型应用于水下焊接质量检测的识别精度可达100%。研究结果可为水下焊接工艺的优化和水下焊件专家知识库的构建提供参考依据。


关键词： 水下焊接,  声信号,  特征提取,  双权值神经网络 
Fig.1 Underwater welding experiment platform
实验参数（焊接速度、焊接电流、板材厚度）水下焊接处理结果
1.2 m/min、110 A、6 mm合格
0.6 m/min、110 A、6 mm合格
0.6 m/min、140 A、6 mm不合格
1.2 m/min、110 A、8 mm不合格
1.2 m/min、140 A、8 mm合格
1.2 m/min、110 A、10 mm不合格
1.2 m/min、160 A、10 mm合格
1.2 m/min、160 A、12 mm不合格
0.6 m/min、160 A、12 mm合格
0.6 m/min、180 A、12 mm合格
Table 1 Measurement results of partial underwater welding experiments
Fig.2 Physical objects of underwater welded steel plate
Fig.3 Amplitude characteristic curve of Butterworth filter
Fig.4 Comparison of spectrogram of acoustic signal before and after filtering
Fig.5 Short-time energy of acoustic signal
Fig.6 Short-time average amplitude of acoustic signal
Fig.7 Short-time average zero crossing rate of acoustic signal
Fig.8 DWNN with fixed structure
Fig.9 Flow of DWNN algorithm introducing PSO
Fig.10 Structure of DWNN for underwater welding quality detection
Fig.11 Underwater welding quality recognition results based on different methods (100 groups of training samples)
Fig.12 Underwater welding quality recognition error based on different methods (100 groups of training samples)
识别模型平均误差最大误差均方误差
DWNN0.002 20.011 40.000 387
SVM0.075 20.988 30.011 039
BPNN0.062 50.876 10.008 856
Table 2 Error comparison of different underwater welding quality recognition models
Fig.13 Underwater welding quality recognition results based on different methods (20 groups of training samples)
Fig.14 Underwater welding quality recognition error based on different methods (100 groups of training samples)
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