Automated identification of steel weld defects,a convolutional neural network improved machine learning approach  

作　　者：Zhan SHU Ao WU Yuning SI Hanlin DONG Dejiang WANG Yifan LI 

机构地区：[1]School of Mechanics and Engineering Science,Shanghai University,Shanghai 200444,China [2]School of Civil Engineering,Shanghai Normal University,Shanghai 201418,China [3]Shanghai PinlanData Technology Co.,Ltd.,Shanghai 200072,China

出　　处：《Frontiers of Structural and Civil Engineering》2024年第2期294-308,共15页结构与土木工程前沿（英文版）

基　　金：support of Shanghai Pinlan Data Technology Co.,Ltd.,and Open Fund of Shanghai Key Laboratory of Engineering Structure Safety,SRIBS(No.2021-KF-06).

摘　　要：This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects,including lack of the fusion,porosity,slag inclusion,and the qualified(no defects)cases.This methodology solves the shortcomings of existing detection methods,such as expensive equipment,complicated operation and inability to detect internal defects.The study first collected percussed data from welded steel members with or without weld defects.Then,three methods,the Mel frequency cepstral coefficients,short-time Fourier transform(STFT),and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses.Classic and convolutional neural network-enhanced algorithms were used to classify,the extracted features.Furthermore,experiments were designed and performed to validate the proposed method.Results showed that STFT achieved higher accuracies(up to 96.63%on average)in the weld status classification.The convolutional neural network-enhanced support vector machine(SVM)outperformed six other algorithms with an average accuracy of 95.8%.In addition,random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.

关 键 词：steel weld machine learning convolutional neural network weld defect detection classification task PERCUSSION 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]