A novel detection of weld defects by magneto-optical imaging under combined magnetic field

To overcome the shortcomings of existing magneto-optical imaging, such as the saturation of an image under a constant magnetic field and the ambiguity of an image under an alternating magnetic field, imaging using a combined magnetic field is presented in this research. Weld defect samples include a laser-cut groove, a wire-cut penetrating groove, a pit and a Z-shaped crack. Magneto-optical imaging experiments were carried out under different magnetic fields. Contour extraction and standard deviation calculations were carried out for all magneto-optical images and the maximum standard deviation of the laser-cut groove under an alternating magnetic field was 20.9, which was less than the maximum value of 37.4 under a combined magnetic field. The experimental results show that the contrast of a magneto-optical image obtained under the combined magnetic field is greater than that obtained under the alternating magnetic field for all defects. The proposed combined magnetic field could optimise the magneto-optical imaging effect for weld defects under the existing excitation method to a certain extent.

Weld defect inspection based on machine vision and weak magnetic technology

This study proposes a weld defect inspection method based on a combination of machine vision and weak magnetic technology to inspect the quality of weld formation comprehensively. In accordance with the principle of laser triangulation, surface information about the weldment is obtained, the weld area is extracted using mutation characteristics of the weld edge and an algorithm for identifying defects with abnormal average height in the weld surface is proposed. Subsequently, a welding seam inspection process is developed and implemented, which is composed of a camera, a structured light sensor, a magnetic sensor and a motion control system. Inspection results from an austenitic stainless steel weldment show that the method combining machine vision and magnetism can identify defect locations accurately. Comprehensive analysis of the test results can effectively classify surface and internal defects, estimate the equivalent sizes of defects and evaluate the quality of weld formation in multiple dimensions.

Pengaruh Variasi Arus pada Pengelasan Baja ST37 Menggunakan Las Shield Metal Arc Welding (SMAW) dengan Posisi Pengelasan 3F

Welding is a very important part in the development and growth of the industry because it has a role in engineering, repair and construction. Shielded metal arc welding (SMAW) is the process of joining two or more materials using a wrapped electrode as heat energy to melt the material. The purpose of this study was to determine the effect and welding defects that arise with current variations so that the optimal welding results obtained from 3 amperes were tested using E6013 electrode diameter 3,2 at the 3F welding position fillet joint. This study uses an experimental method with the material used is St 37 steel with a current variation of 90A, 100A, and 110A. From the research conducted, it was not found optimal welding results where from each ampere tested there was still a weld defect. The defects that occur in the three amperes are caused by the arc, electrode angle, and arc length that exceed the normal limit and are also influenced by the welding speed. From the three variations of the amperage used, the dominant welding results did not occur, namely the 90 amperage, while the 100 amperage welding leg showed good results.

Improving the robustness of DI and PVI further using fast guided filter on radiographic images

To further improve the robustness of the weld defect index (DI) and peak-valley index (PVI), which are key indices for detecting weld defects in radiographic testing (RT) images accurately and reliably, a robust improvement method is proposed, in which a fast guided filter (Fast-GF) is introduced and its effect on the DI and PVI is analysed. In this paper, the principle of the proposed robust improvement method, the related theory of Fast-GF, the definition and the calculational method of the DI and PVI are systematically analysed. Taking some practical RT images from industrial welding as an example, smoothing experiments with different filters and comparative computational experiments for the DI and PVI both with and without Fast-GF are carried out. The experimental results show that the robustness of the DI and PVI is further improved by the proposed robust improvement method, which is a desirable outcome. More specifically, the values of the DI and PVI are computed accurately and reliably regardless of some non-uniform distribution of grey levels, noise, irregular surfaces and artefacts in the RT images.

Development of Radiographic Image Classification System for Weld Defect Identification using Deep Learning Technique

Weld defect identification from radiographic images is a crucial task in the industry which requires trained human experts and enough specialists for performing timely inspections. This paper proposes a deep learning based approach to identify different weld defects automatically from radiographic images. To employ this a dataset containing 200 radiographic images labelled for four types of welding defect- gas pore, cluster porosity, crack and tungsten inclusion is developed. Then a Convolutional Neural Network model is designed and trained using this database.