Acoustic Emission Waveform Analysis From Weld Defects in Steel Ring Samples

Shiwa, Mitsuharu; Yamaguchi, Akio; Satõ, Mitsuo; Murao, Seiichi; Nagai, Masashi

doi:10.1115/1.2883671

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…In order to obtain the characteristic of scale invariance on the edges of welding pool, the scale space was composed of four inner layers ci and four middle layers di(i=0, 1,2,3) in the frame structure of brisk feature detection [25]. Each inner layer image was obtained by 0.5 times down sampling of the previous inner layer image, where the original image was corresponded to the c0 layer.…”

Section: Brisk Feature Point Detectionmentioning

confidence: 99%

“…It means that welding pool surface includes important visual information for the skilled welders to control GMAW process. To decrease the workload and occupational risks of welders, lots of researches have been done to transfer the working experience of skilled welders into intelligent control system in the past few decades [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Three-dimensional reconstruction of welding pool surface by binocular vision

Chen

2020

Preprint

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 Abstract: Welding pool geometry includes plenty of welding quality information. The observation and reconstruction of the welding pool surface is the basis of developing intelligent control system for welding process to substitute skilled welders. The binocular vision system was ameliorated to capture images of welding pool surface by suppressing the strong arc interference during gas metal arc welding (GMAW). Combining and improving the algorithms of speeded up robust features, binary robust invariant scalable keypoints and KAZE, the feature information of points (i.e. RGB value, pixel coordinates and so on) was extracted as the feature vector of the welding pool surface. Based on the characteristic of welding images, the mismatch elimination algorithm was developed to increase the accuracy of image matching algorithms. The world coordinates of matching feature points was calculated to reconstruct the 3D shape of the welding pool surface. The effectiveness and accuracy of reconstruction for welding pool surface were verified by the experimental results.

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Section: Brisk Feature Point Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-dimensional reconstruction of welding pool surface by binocular vision

Chen

2020

Preprint

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“…Numerous research in weld quality monitoring and diagnosis has been carried out on single-conditioned welding tasks. Many research works have been focusing on offline diagnosis of weld quality [9][10][11], such as using acoustic emission method [10], X-ray image analysis [11], and so forth. Recently, advanced robotic welding has been applied increasingly in various industries, and online For this purpose, various sensor fusion and signal processing techniques have been carried out [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design of adaptive weld quality monitoring for multiple‐conditioned robotic welding tasks

Xia

Pang

Mamun

et al. 2021

Asian Journal of Control

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Multiple-conditioned welding monitoring is a challenging issue in complex robotic welding tasks. In practice, the monitoring system has to be sensitive to different welding conditions (WCs) and weld quality changes. In this paper, a swing high temperature sensor system is used in order to obtain the temperature distribution curve under different WCs. A sigmoid feature extraction (SFE) method is proposed to obtain the geometric features of the temperature distribution curve, and a weld monitoring algorithm is proposed for multiple-conditioned welding tasks using multi-layer perceptron (MLP) classifier and Kalman filter-based Gaussian probability density function (PDF) prediction for the probabilistic weld quality estimation. When there are unknown WCs, the proposed method uses an efficient incremental learning for the MLP and an online maximum likelihood estimation for the Gaussian models of the unknown WCs. The experimental results show that the proposed framework can accurately reflect the weld quality changing in both single-WC and multiple-WC tasks. In addition, the proposed adaptive updating methodology can achieve comparable performance with unknown WCs, as compared to the results with knowing all the WCs.

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“…Thus, numerous sensing methods have been developed and applied in automated welding to acquire weld quality information from the observation of weld pool. Sensing techniques including pool oscillation, ultrasonic, infrared sensing and acoustic emission ( [8][9][10][11][12]) have been studied to estimate the weld pool status.…”

Section: Introductionmentioning

confidence: 99%

A mobile sensing system for real-time 3D weld pool surface measurement in manual GTAW

Zhang

Xiao

Zhang

2016

Meas. Sci. Technol.

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An innovative mobile sensing system has been developed to non-intrusively monitor the manual pipe gas tungsten arc welding (GTAW) process. The system consists of a projective torch held by a welder, and a sensory helmet on the welder’s head. The three-dimensional (3D) weld pool surface is effectively measured in real-time as the welder performs the weld despite the movements of the torch and the helmet. In this study, the sensing system is first analyzed by numerical simulations in which the adjustment boundaries of the torch and helmet, i.e. their translation and orientation ranges, for effective sensing have been determined. Then, the sensing system is further evaluated using a simulation platform in which the movements of the helmet/welder’s head is mimicked by a tripod head with 6 degree-of-freedom (DOF), and a convex spherical mirror with a comparable size of a typical weld pool in a GTAW process is applied as a weld pool substitute. The effectiveness of the proposed system is validated by successfully capturing laser reflections from the mirror without great constraints on the welder’s movements of the torch and the helmet. Based on the analysis of the spatial relations among the torch, the helmet and the weld pool, an innovative real-time algorithm is proposed to reconstruct the 3D weld pool surface. The effectiveness and robustness of the algorithm have been verified by accurately reconstructing the convex spherical mirror surface despite the movements of the torch and the helmet in the simulation platform.

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Acoustic Emission Waveform Analysis From Weld Defects in Steel Ring Samples

Cited by 10 publications

References 2 publications

Three-dimensional reconstruction of welding pool surface by binocular vision

Three-dimensional reconstruction of welding pool surface by binocular vision

Design of adaptive weld quality monitoring for multiple‐conditioned robotic welding tasks

A mobile sensing system for real-time 3D weld pool surface measurement in manual GTAW

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