{"title":"利用超声相控阵对超声焊接汽车线束端子进行质量分类","authors":"Xu He, Xiaobin Jiang, Runyang Mo, Jianzhong Guo","doi":"10.1134/S1061830924600138","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality Classification of Ultrasonically Welded Automotive Wire Harness Terminals by Ultrasonic Phased Array\",\"authors\":\"Xu He, Xiaobin Jiang, Runyang Mo, Jianzhong Guo\",\"doi\":\"10.1134/S1061830924600138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":764,\"journal\":{\"name\":\"Russian Journal of Nondestructive Testing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Russian Journal of Nondestructive Testing\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S1061830924600138\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Nondestructive Testing","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1134/S1061830924600138","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
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.
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).