Acoustic Emission Assessment of Impending Fracture in a Cyclically Loading Structural Steel

Растегаев, И. А.; Danyuk, A. V.; Afanasyev, M. A.; Мерсон, Д. Л.; Berto, Filippo; Vinogradov, Alexei

doi:10.3390/met6110266

Cited by 7 publications

(10 citation statements)

References 14 publications

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“…In a study, Shankhwar et al [128] used a trilinear interpolation method, an interactive Mixed Reality (MR)-based user interface, a Gradient Boosted Regression Tree (GBRT) model, and FEA to visualize the results of hand metal arc welding. In a cyclically loaded structural steel, the Acoustic Emission (AE) examination of proximate fracture was carried out by Rastegaev et al [129] using AE data. A combination of criteria based on power spectrum and pattern recognition, as well as energy data and spectral density functions was used.…”

Section: Random/hybrid Based Methodsmentioning

confidence: 99%

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

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Section: Random/hybrid Based Methodsmentioning

confidence: 99%

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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“…An analysis of the existing methods and means for determining the degradation of the mechanical properties of materials during the operation of structures in real time showed that they are all intended for the study of one type of deformation: tension [ 37 , 38 , 39 ] or bending [ 40 , 41 , 42 , 43 ], while under real conditions loading forces act on the material, under combined conditions of deformation [ 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Relative Workmentioning

confidence: 99%

An Acoustic Emission Method for Assessing the Degree of Degradation of Mechanical Properties and Residual Life of Metal Structures under Complex Dynamic Deformation Stresses

2021

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An acoustic emission method for assessing the degree of degradation of mechanical properties under conditions of complex dynamic deformation stresses is proposed. It has been shown that changing the operating conditions of metal structures, peak loads, external collisions, and thermally changing loads, which cannot be taken into account, leads to uncertainty and unpredictable structural changes in the material. This in turn makes it difficult to identify the state of the structure material to ensure trouble-free operation of the equipment. Changes in the mechanical properties under difficult loading conditions are identified by polynomial approximation of the results of AE measurements and the construction of boundary curves separating the operability region from the fracture region. This is achieved by approximating the experimental dependences of the acoustic parameters for various types of loading. This approach significantly expands the capabilities of the technical means of identification systems of metal structures, and in particular, allows the current state of the equipment and its suitability for further operation to be assessed without stopping the equipment in real time. It is of interest not only to fix the damage, but also to diagnose the processes of reducing the mechanical properties during the operation of the equipment.

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“…Due to its sensitivity to microstructure parameters, the AE method of application makes it possible to detect violations of the structural composition in the material [17][18][19]. In a number of experimental research studies [20][21][22][23][24][25], it was found that the AE method If such dissimilar welded joints are operated for a long time at high temperatures, the diffusion processes will proceed intensively, and as a result the interlayers will increase in size. The presence of such interlayers negatively affects the mechanical properties of the welded joint [7].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Features of Acoustic Emission in Tensile Testing of Dissimilar Welded Joints of Pearlitic and Austenitic Steels

et al. 2021

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This paper presents a study of acoustic emission (AE) during the deformation of dissimilar welded joints of austenitic steel to pearlitic steel. One of the specific problems in these welded joints is the presence of decarburized and carbide diffusion interlayers, which intensively increase in width during long-term high-temperature operation. The presence of wide interlayers negatively affects the mechanical properties of welded joints. Moreover, welded defects are difficult to diagnose in welded joints containing interlayers: due to the high structural heterogeneity, interlayers create structural noises that can hinder the detection of defects such as cracks, pores, or a lack of penetration. The AE method may become a complex decision for diagnosing dissimilar welded joints due to applicability to the testing of heterogenic materials with a complex microstructure. Specimens cut from dissimilar welded joints of austenitic steel to pearlitic steel were tested by tension to rupture, with parallel AE data registration. According to the research results, the characteristic features of the AE were revealed for specimens containing defects in the form of lack of penetration as well as for specimens with diffusion interlayers. The results obtained show that the AE method can be used to test both typical welding defects and diffusion interlayers in welded joints of steels of different structural classes.

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Acoustic Emission Assessment of Impending Fracture in a Cyclically Loading Structural Steel

Cited by 7 publications

References 14 publications

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

An Acoustic Emission Method for Assessing the Degree of Degradation of Mechanical Properties and Residual Life of Metal Structures under Complex Dynamic Deformation Stresses

Features of Acoustic Emission in Tensile Testing of Dissimilar Welded Joints of Pearlitic and Austenitic Steels

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