Recently, studies have been conducted on the classification of abnormal laser welding images using deep learning, offering promising prospects for effective welding defect detection. In this study, we propose a nondestructive method to assess the welding quality exclusively based on the final product, even when the product history is …
Learn MoreThere are many flaws in welding images such as noise, low contrast, and blurred edges, which affect feature extraction from welding defect regions and impede classification and recognition of welding defects. To deal with the complexity of welding defect images, this paper proposes an effective method for extracting the features of welding defect …
Learn MoreLaser-arc hybrid welding (LAHW), being a high-efficiency with excellent properties of high welding speed, deep penetration, and good bridging performance, has been paid close attention by domestic and overseas scholars. So far, the lack of suppression and detection of welding defects is still considered the critical technical obstacle that affects its …
Learn MoreA semantic-segmentation-based three-stage weld classification method using DL on radiographic images is proposed by Chang et al. [30] for visual defect classification including porosity and cracks ...
Learn MoreThe welding technique is the primary cause. Too much heat along the edges, erratic movements while welding, and holding an arc that is too long are common causes of an undercut defect. You can remove the weld and lay another one while using a better welding technique to fix the problem. 4. Slag Inclusion.
Learn MoreA weld defect was introduced into the weld joint of the battery cap by controlling the welding laser power. A Rayxion IPG continuous laser welding machine …
Learn MoreIn the laser welding AOI system, the welding area images were obtained using a CMOS digital camera (BASLER Basler acA2500-14um camera and UTRON HS2514J lens), and a white annula r LED
Learn MoreThis article proposes a lightweight deep-learning algorithm called MGNet for detecting welding defects in the current collectors. We introduce a lightweight MDM …
Learn MoreAi et al. [16] proposed a welding defect evaluation index considering the geometrical characteristics of weld including longitudinal defects, transverse defects and fusion area in the laser welding of dissimilar materials. It …
Learn MoreHowever, the most common problems such as microvoid, excessive material loss and tunnel defect have not yet been solved for welding dissimilar aluminum alloys by FSW [5,14,15,17,21]. Therefore ...
Learn MoreBased on field application, the following results were obtained. First, this automatic defect identification technology is applicable to quality identification and evaluation of various defects in ...
Learn MoreTowards the trend and demand of automatic welding inspection in industry, a composite vision system enabling simultaneous 3D-depth and 2D-gray imaging of the bead surface is constructed to detect typical surface defects of aluminum alloy weld beads. In this vision system, the structured laser light is responsible for obtaining 3D …
Learn MoreBy pre-processing the line scan 2D images, the defect location distribution is obtained, and then the images near the abnormal points are reconstructed …
Learn MoreLaser-arc hybrid welding (LAHW), being a high-efficiency with excellent properties of high welding speed, deep penetration, and good bridging performance, has been paid close attention by domestic and overseas scholars. So far, the lack of suppression and detection of welding defects is still considered the critical technical obstacle that affects its …
Learn MoreAutomatic detection of welding flaws based on deep learning methods has aroused great interest in the non-destructive testing. However, few studies focus on the …
Learn MoreMetal storage tanks will degrade because of interactions with petrochemical materials in the tanks. With the passage of time, corrosion and cracks may form, leading to the occurrence of leakage and further causing economic losses, serious safety accidents and environ-mental pollution [1, 2].To avoid environmental damage, oil …
Learn Moredeep learning algorithm is employed to detect the weld defects, and the Convolutional Neural Networks (CNNs) are utilized to. recognize the image features. The T ransfer Learning (TL) is. adopted ...
Learn MoreThe existing works listed in Table 3 used similar weld defect radiograph images for training and testing the classifier models. In particular, [18, 30] utilized the same GDXray image dataset in ...
Learn MoreTo detect the weld defects, laser streak images of different weld defects are collected and two types of data augmentation are used to reduce overfitting caused by the limited training...
Learn MoreThe proposed approach to detecting weld defects follows a general pattern recognition scheme based on three steps: segmentation, feature extraction and classification. That is, in our case, 1 ...
Learn MoreAn efficient support vector machine-based approach has been developed for automatic welding fault detection in real-time X-ray welding images, and cross-validation was used to find the best ...
Learn MoreRead Also : Welding Joints: 5 Types and Tips for Top-Quality Results Weld Defects at Root There are 5 types of defects in the root. Below are the five types of defects. Incomplete Root Penetration or …
Learn MoreSupport Vector Machines is trained with different kernel functions and found that linear and quadratic kernel function classify defect weld and good weld with accuracy of 95.8%. View Show abstract
Learn MorePreventive measures. Manual arc welding. (1) The welding rod is poor or wet. (2) The weldment has moisture, oil stain or rust. (3) Welding speed is too fast. (4) The current is too strong. (5) The arc length is not suitable. (6) The thickness of weldment is large and the metal cooling is too fast.
Learn MoreThis 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 …
Learn MoreManual inspection, analysis and evaluation of welding defect images is difficult due to the non-uniformity in their shape, position, and size. Hence the use of …
Learn MoreIt is evident from the SEM images (discussed) that internal weld porosity (i.e. tunnel defects, cavity, and voids) were formed in the SZ. In order to study the 3-D tomographical and volumetric study of weld pores, XMCT scans have been carried out on welded samples for different values of α and ω and the results are shown in Fig. 9, Fig. 10 .
Learn MoreAfter conducting a comprehensive literature analysis through the literatures, it can be summarized that the automatic detection systems for weld defect mainly involve several technologies: image ...
Learn MoreAn automatic computer-aided detection system based on Support Vector Machine (SVM) was implemented to detect welding defects in radiographic images to show the efficiency of the proposed method based on the support vector machine. Radiographic testing method is often used for detecting defects as a non-destructive testing method. In this paper, an …
Learn MoreWith the rapid progress and innovation of computer vision, image processing and other related disciplines and theoretical methods, machine vision has been more and more widely used. In the process of welding production, there are many kinds of weld defects, such as crack, incomplete penetration, incomplete fusion, porosity and …
Learn More1 Introduction. In modern manufacturing industries, welding defect detection using X-ray images plays a crucial role. Welding is a common method for joining metal components; however, defects within welds can lead to reduced structural strength, fracture, or failure, thereby adversely affecting product quality and safety. Therefore, …
Learn MoreTo solve the problem of laser welding defect detection for lithium battery poles and to realize the automatic welding defect detection in the industry, this paper proposed an end-to-end improvement algorithm …
Learn MoreThis section first introduces the dataset in this paper, followed by a description of the algorithms and principles used. 2.1 Dataset. This paper uses the TIG Al5083 weld defect public dataset by Bacioiu et al. [] in 2019.The dataset was obtained by tracking the TIG Al5083 welding process using an HDR camera facing directly towards …
Learn MoreIn this paper, we present an automatic detection model for weld defect bas ed on deep neural network. that extracts the intrinsic features of x-ray images. Several experiments have been conducted ...
Learn MoreBattery surface reconstruction can inspect the quality of the weld instead of relying on human inspection. This paper proposes a defect detection method in the …
Learn MoreThis Section quantitatively compares the three presented welding techniques for connecting battery cells in terms of electrical contact resistance, ultimate tensile force and heat input into the cell. In this comparison section, only the results for CuZn37 test samples according to Fig. 2 are discussed, because CuZn37 can be welded …
Learn MoreFill crater before extinguishing the arc; use a welding current decay device when. terminating the weld bead. Causes: Heat-Affected Zone. Remedies: Heat-Affected Zone. Hydrogen in welding atmosphere. Use low-hydrogen welding process; preheat and hold for 2 hour after welding or.
Learn MoreThis is because, unlike other welding, laser welding does not add material to the sheet metal, and hardly produces obvious defects and residues. High-frequency laser welding locally melts metallic ...
Learn MoreFirstly, projecting the image of weld defects in the training set into a two-dimensional space using multidimensional ... Tungsten inert g as welding, Machine learning, Weld defect. 1 Introduction ...
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