Abstract:The article presents the maps of xx stress component and compares values of analytical and numerical calculations for the stress intensity factor range of welded specimens with fillet welds which subjected to cyclic bending. The tests were performed under constant value of moment amplitude Ma= 9.20 N·m and stress ratio R = σmin/ σmax= −1. The specimens were made of drag steel rod S355. The specimens were solid and welded. The numerical models were simulated with ABAQUS suite and numerical calculations performe… Show more
“…A fatigue fracture is defined as a "partial or complete fracture due to its inability to withstand non-visible stresses applied rhythmically, repeatedly, and below the threshold" [1] Fatigue fractures occur due to the accumulation of stress-induced microfractures. One should investigate the stress intensity factor and the outcomes of both experimental (analytical) and numerical calculations for the range of stress intensities in order to evaluate the effects of cyclic fatigue on metallic specimens with diverse physical features [2]. Using heterogeneous image fusion to reduce picture-related disturbances in intensity or range image data and to reduce uncertainties through cross-domain feature correlation, a deep convolutional neural network-based crack segmentation methodology was provided [3].…”
Section: Introductionmentioning
confidence: 99%
“…To describe and present the data in a more understandable way, Equation (1) undergoes linearization, which is presented in Equation (2). In this way, the data obtained can be graphed and interpreted.…”
Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fractures according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). In this investigation, mechanical fatigue tests were carried out on specimens smaller than 4 mm2, made of different industrial materials. Their subsequent analysis was performed through precision computed tomography, in search for microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with resolutions of 480 × 854 or 960 × 960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those without are classified using the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images containing this feature are interpreted using image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and microfracture-type metrics. All values are obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution. The growth of microfractures will be used to define trends in the development of fatigue fractures through the studies presented.
“…A fatigue fracture is defined as a "partial or complete fracture due to its inability to withstand non-visible stresses applied rhythmically, repeatedly, and below the threshold" [1] Fatigue fractures occur due to the accumulation of stress-induced microfractures. One should investigate the stress intensity factor and the outcomes of both experimental (analytical) and numerical calculations for the range of stress intensities in order to evaluate the effects of cyclic fatigue on metallic specimens with diverse physical features [2]. Using heterogeneous image fusion to reduce picture-related disturbances in intensity or range image data and to reduce uncertainties through cross-domain feature correlation, a deep convolutional neural network-based crack segmentation methodology was provided [3].…”
Section: Introductionmentioning
confidence: 99%
“…To describe and present the data in a more understandable way, Equation (1) undergoes linearization, which is presented in Equation (2). In this way, the data obtained can be graphed and interpreted.…”
Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fractures according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). In this investigation, mechanical fatigue tests were carried out on specimens smaller than 4 mm2, made of different industrial materials. Their subsequent analysis was performed through precision computed tomography, in search for microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with resolutions of 480 × 854 or 960 × 960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those without are classified using the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images containing this feature are interpreted using image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and microfracture-type metrics. All values are obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution. The growth of microfractures will be used to define trends in the development of fatigue fractures through the studies presented.
The paper presents the test results on the fatigue crack growth under cyclic bending specimens at constant moment amplitude made of S355 steel with fillet welds. Plane specimens with stress concentrators in form of the external two-sided blunt notches were tested. The tests were performed under constant value of the stress ratio R = –1 without and after heat treatment. The article also presents the test results of the microstructure of welded joints taking into account changes in the material after heat treatment and the impact of these changes on the fatigue life of specimens.
Keywords: welded joints, fatigue cracks length, number of cycle, bending, microstructure
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