利用超声相控阵对超声焊接汽车线束端子进行质量分类

IF 0.9 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
Xu He, Xiaobin Jiang, Runyang Mo, Jianzhong Guo
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引用次数: 0

摘要

摘要 针对汽车线束端子中多股铜电缆的超声焊接接头,提出了一种超声无损评价技术。采用 32/128 超声相控阵系统采集线束接头脉冲回波的完整矩阵数据。提取时域、频域和时频域的特征值,并通过机器学习对线束接头质量进行分类。首先,在不同的焊接参数下制备了 28 个线束端子接头样本,其中 14 个为合格(OK),14 个为不合格(NG)。然后使用线性阵列探头 5L32-0.6 × 10 采集和预处理这些接头的完整矩阵数据,得到 11 200 个回波信号。采用主成分分析算法对数据进行降维和去噪处理。最后,使用机器学习算法对模型进行训练和验证。比较了逻辑回归(LR)、K-近邻(KNN)、决策树(DT)、奈夫贝叶(NB)、支持向量机(SVM)和神经网络(NN)等传统算法的准确性和性能。在这项研究中,KNN 和 NN 表现良好。在测试集中,KNN 和 NNN 的准确率达到了 90%。研究表明,回声特征能有效识别关节质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Quality Classification of Ultrasonically Welded Automotive Wire Harness Terminals by Ultrasonic Phased Array

Quality Classification of Ultrasonically Welded Automotive Wire Harness Terminals by Ultrasonic Phased Array

Quality Classification of Ultrasonically Welded Automotive Wire Harness Terminals by Ultrasonic Phased Array

An ultrasonic nondestructive evaluation technique is proposed for ultrasonically welded joints of multi-strand copper cables in automobile wire harness terminals. The 32/128 ultrasonic phased array system is used to acquire the complete matrix data of the pulse-echo of the wire harness joints. The eigenvalues of the time, frequency, and time-frequency domains are extracted, and the wire harness joint quality is classified by machine learning. Firstly, 28 wire harness terminal joint samples were prepared 14 under different welding parameters; 14 were okay (OK), and were negative (NG). Then a linear array probe 5L32-0.6 × 10 is used to collect and preprocess the complete matrix data in these joints, and 11 200 echo signals are obtained. A principal component analysis algorithm was employed for data dimensionality reduction and denoising. Finally, machine learning algorithms were used to train and verify the model. The accuracy and performance of the traditional algorithms such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Neural Network (NN) were compared. The KNN and NN perform well in this study. In the test set, the accuracy of KNN and NN reached 90%. The study showed that echo features could effectively identify joint quality.

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来源期刊
Russian Journal of Nondestructive Testing
Russian Journal of Nondestructive Testing 工程技术-材料科学:表征与测试
CiteScore
1.60
自引率
44.40%
发文量
59
审稿时长
6-12 weeks
期刊介绍: Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).
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