Automatic welding imperfections detection in a smart factory via 2-D laser scanner

Bologna, Francesco; Tannous, Michael; Romano, Donato; Stefanini, Cesare

doi:10.1016/j.jmapro.2021.10.046

Cited by 19 publications

(4 citation statements)

References 39 publications

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“…The willingness to test connectors against other criteria is associated with the expansion of the system, but it should be based on artificial neural networks. Some such trends could be found in the literature [28,29].…”

Section: Discussionmentioning

confidence: 59%

Development of the automatic method of detection and grouping of external welding imperfections

Brzeskot

Łatka

2022

J. Phys.: Conf. Ser.

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The non-destructive testing of welded joints is a key issue in welding technology and processes. It is especially crucial for estimation of product quality. Among others methods, visual testing is the most fundamental. In most cases, it is made manually, which can introduce some problems resulting from the lack of objectivity and fatigue of the controlling person. This paper is an attempt of changing a mode of visual inspection. The proposed system is based on image analysis of welded joints. The welding imperfections were correlated with quality levels according to the ISO 5817 standard. For the original inspection system, six the most fundamental imperfections have been selected. The main idea of this system is a detection of selected welding imperfections and, based on acceptance criteria, assigning welded joints for the appropriate quality level. In order to validate the correct operation of the proposed system, the same welded joints have been subjected to conventional manual visual inspection. The compliance of the results was more than 90%, and the speed of visual examinations was more than 10 times higher than in the manual method.

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Section: Discussionmentioning

confidence: 59%

Development of the automatic method of detection and grouping of external welding imperfections

Brzeskot

Łatka

2022

J. Phys.: Conf. Ser.

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“…As early as the mid-1960s, a large number of researchers began to study the expert system. Welding technology is the product of multi-disciplines, ranging from weldability analysis before welding [2,3] and preparation of welding process documents [4] to the selection of process parameters in the welding process [5][6][7][8] and real-time control of welding [9] , defect diagnosis and treatment after welding [10] , welding quality assessment [11,12] and calculation of welding material consumption [13] . Each stage involves a large amount of data, knowledge and models, which is an ideal field for expert system application [14,15] .…”

Section: Introductionmentioning

confidence: 99%

GMAW welding procedure expert system based on machine learning

Wang¹,

Chen²,

Sun³

et al. 2023

Intell Robot

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In order to simplify the robot preparation before welding and improve the automation of the whole welding process, an intelligent expert system for Gas Metal Arc Welding is designed in this paper. In the system, the user inputs the initial welding information and the output interface displays suitable welding procedure parameter schemes. The user can choose the schemes according to the actual requirements or directly generate the welding procedure specification required by the enterprise format for direct use. In addition, the system also combines the database technology and XGBoost algorithm in the field of machine learning, migrates the model trained on the data set to predict the welding raw data, accumulates more data for daily use to optimize the model, which makes the whole system more systematic and intelligent, and achieves the goal of more accurate use.

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“…Ding et al [12] proposed the wavelet soft and hard threshold compromise denoising method, Patil et al [13] proposed the techniques of local binary pattern in which local binary code describing region, generating by multiplying threshold with specified weight to conforming pixel and summing up by grey-level co-occurrence matrix to extract statistical texture features, Boaretto et al [14] extracted potential defects based on feedforward multilayer perceptron with back propagation learning algorithm. In addition, there are relevant research on weld feature extraction based on computer vision [15,16]. Although they have made some achievements, there are still problems to be solved [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

Zheng¹,

Luo²,

Shen³

et al. 2023

Energy Engineering

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To solve the problems of low precision of weak feature extraction, heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage (UHV) equipment key parts, an automatic feature extraction algorithm is proposed. Firstly, the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method. Then, binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation. Finally, the automatic identification of weld defect area is realized based on the sequential traversal of binary tree. Several characteristic analysis dimensions are established for weld defects of UHV key parts, including defect area, perimeter, slenderness ratio, duty cycle, etc. The experiment using the weld detection image of the actual production site shows that the proposed method can effectively extract the weak feature information of weld defects and further provide reference for decision-making.

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Automatic welding imperfections detection in a smart factory via 2-D laser scanner

Cited by 19 publications

References 39 publications

Development of the automatic method of detection and grouping of external welding imperfections

Development of the automatic method of detection and grouping of external welding imperfections

GMAW welding procedure expert system based on machine learning

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

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