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Relevant bibliographies by topics / Welding Automation Inspection

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Journal articles

Academic literature on the topic 'Welding Automation Inspection'

Author: Grafiati

Published: 11 December 2022

Last updated: 26 January 2023

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Journal articles on the topic "Welding Automation Inspection"

1

Yun, Gwang-ho, Sang-jin Oh, and Sung-chul Shin. "Image Preprocessing Method in Radiographic Inspection for Automatic Detection of Ship Welding Defects." Applied Sciences 12, no. 1 (2021): 123. http://dx.doi.org/10.3390/app12010123.

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Welding defects must be inspected to verify that the welds meet the requirements of ship welded joints, and in welding defect inspection, among nondestructive inspections, radiographic inspection is widely applied during the production process. To perform nondestructive inspection, the completed weldment must be transported to the nondestructive inspection station, which is expensive; consequently, automation of welding defect detection is required. Recently, at several processing sites of companies, continuous attempts are being made to combine deep learning to detect defects more accurately.

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2

Li, Yan, Miao Hu, and Taiyong Wang. "Visual inspection of weld surface quality." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5075–84. http://dx.doi.org/10.3233/jifs-179993.

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Welding is an important method for modern material processing. In actual processing, due to the influence of processing accuracy and welding thermal deformation, various defects often appear in the appearance of the weld. At present, visual inspection is mainly used for the appearance inspection of welds. The detection of weld defects mainly depends on the work experience of the staff. Based on the above background, the purpose of this article is to visually inspect the weld surface quality. This article uses visually obtained fringe images of weld contours as information sources to explore a

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3

Majeed, Tanveer, Mohd Atif Wahid, and Faizan Ali. "Applications of Robotics in Welding." International Journal of Emerging Research in Management and Technology 7, no. 3 (2018): 30. http://dx.doi.org/10.23956/ijermt.v7i3.9.

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An Industrial robot is reprogrammable, automatically controlled, multifunctional manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation applications. Technical innovations in robotic welding has facilitated manual welding processes in sever working conditions with enormous heat and fumes to be replaced with robotic welding. The robotic welding has greater capability to control robot motion, welding parameters and enhanced wrong detection and wrong correction. Major difficulties in robotic welding are joint edge inspection,

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4

Nikolenko, S., Svetlana Sazonova, and N. Akamsina. "AUTOMATION OF THE PROCESS OF QUALITY CONTROL OF WELDED JOINTS." Modeling of systems and processes 13, no. 3 (2020): 76–85. http://dx.doi.org/10.12737/2219-0767-2020-13-3-76-85.

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The analysis of the electric arc welding process is performed. The main types and causes of defects in welded joints have been identified. The main non-destructive methods of control of welded joints are analyzed. The revealed advantages and disadvantages of each method. The method of ultrasonic testing is considered in sufficient detail. Shown on the practical experience of ultrasonic inspection of welded joints by a flaw detector with antenna arrays. The features of application of the program for PC «USD-soft», which allows automating the control of welded seams, are considered.

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5

Ramesh Krishnan, S. "Automatic Detection and Characterization of Weld Defects Using CNN Algorithm in Machine Learning." Asian Review of Mechanical Engineering 10, no. 1 (2021): 26–35. http://dx.doi.org/10.51983/arme-2021.10.1.2937.

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Conventional radiographic technique uses visual inspection of scanned output for defect detection. This makes the inline testing of products time consuming and hectic. Convolutional Neural Network (CNN) algorithm in machine learning can be used for the automation of defect detection in radiography thereby reducing human intervention and associated delays. By the use of robotics the welding parameters can be adjusted and the issue of welding defects can be resolved. By combining the two, the defect detection process can be modified into a digital manufacturing process. A dataset created from ra

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6

Oh, Sang-jin, Min-jae Jung, Chaeog Lim, and Sung-chul Shin. "Automatic Detection of Welding Defects Using Faster R-CNN." Applied Sciences 10, no. 23 (2020): 8629. http://dx.doi.org/10.3390/app10238629.

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In the shipbuilding industry, the non-destructive testing for welding quality inspection is mainly used for the permanent storage of the testing results and the radio-graphic testing which can visually inspect the interior of the welded part. Experts are required to properly detect the test results and it takes a lot of time and cost to manually Interpret the radio-graphic testing image of the structure over 500 blocks. The algorithms that automatically interpret the existing radio-graphic testing images to extract features through image pre-processing and classify the defects using neural net

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7

Tsuzuki, Ryoichi. "Development of automation and artificial intelligence technology for welding and inspection process in aircraft industry." Welding in the World 66, no. 1 (2021): 105–16. http://dx.doi.org/10.1007/s40194-021-01210-3.

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8

Adamczak, Arkadiusz. "Detection Method of Welding Defects in a Robotic Station Using the Deep Neural Network." Pomiary Automatyka Robotyka 25, no. 1 (2021): 67–72. http://dx.doi.org/10.14313/par_239/67.

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Modern automation and robotization of production processes requires new and fast methods of product quality control. In the case of arc welding in robotic systems, where the production process takes place in large series, it is important to quickly control the correctness of the weld. Based on visual data, the system should be able to automatically determine whether a given weld meets the basic quality requirements, and thus be able to stop the process in the event of identified defects. The article presents the results of research on the creation of a visual method for assessing the correctne

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9

Liu, Xiaoqin, Xing Wu, Chang Liu, and Tao Liu. "RESEARCH ON CONDITION MONITORING OF SPEED REDUCER OF INDUSTRIAL ROBOT WITH ACOUSTIC EMISSION." Transactions of the Canadian Society for Mechanical Engineering 40, no. 5 (2016): 1041–49. http://dx.doi.org/10.1139/tcsme-2016-0086.

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Articulated industrial robots are widely used for factory automation, e.g., the car manufacture industry. As other machines, the transmission system of industrial robot is prone to fail after long term operation. Speed reducer is the key component of the transmission system, and it is important to detect its incipient faults to avoid breaking down. However, until now, few techniques have been proposed to diagnose the reducer without disassembly. Our research on monitoring and diagnosing the speed reducer of industrial robot is reported in this paper. The technique combines inspection method of

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10

Cojocaru, Radu, Lia Nicoleta Boțilă, Ion Aurel Perianu, and Alexandru Adrian Geană. "Considerations Regarding the Extension of the Service Life of Metal Wear Components from Urban Means of Rail Transport." Engineering Innovations 2 (June 20, 2022): 31–39. http://dx.doi.org/10.4028/p-o0d13z.

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The paper presents proposals for technical solutions based on the interest shown by transport companies in the country to reduce the cost of maintenance of means of transport by increasing the service life of metal components that, in traffic, are subject to a severe process of wear degradation. Specifically, the paper proposes solutions to solve these problems in the case of urban public transport by rail (tram). The results obtained by ISIM Timisoara (implemented at public transport companies in the country) in the field of reintroduction into operation of used metal components from trams ar

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