A novel correlation models for predicting stress intensity factor of semi-elliptical crack in welded pipelines under cyclic internal pressure based on Bézier base XIGA

Shoheib, Mohammad Mehdi

doi:10.1016/j.ijpvp.2023.105052

Cited by 3 publications

(4 citation statements)

References 44 publications

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“…When examining the unique features of fatigue failure for steel pipes with various strength levels, it was discovered that the stress range no longer influences the number of cycles once maximum tensile stress is reduced to 6-8%, However, regression equations that can be used to forecast the remaining life of a specific pipeline section were created [25]. Similarly, the number of required analyses to obtain SIF and FL in pipes would become less by about 20%, if the technique by Shobeib was implemented [67].…”

Section: Resultsmentioning

confidence: 99%

“…In a study by Shoheib [67] to analyze the effects of cyclic internal pressure on pipelines. A new correlation model was implemented which showed an acceptable accuracy in the prediction of SIF and FCGR in the pipes.…”

Section: Numerical Approach-based Methodsmentioning

confidence: 99%

“…Tensile-shear strength, indentation depth and nugget size of the specimens [61], cyclic internal pressure [67].…”

Section: Regressionmentioning

confidence: 99%

“…Spot welding [60,61], soldering [64], GMAW [65] Automotive [58], electronics [64], pipelines [67] Neural network Steel [69,73,77,79,81,[83][84][85]87], aluminum [74,82], dissimilar metals [75,76].…”

Section: Regressionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Numerical Approach-based Methodsmentioning

confidence: 99%

“…Tensile-shear strength, indentation depth and nugget size of the specimens [61], cyclic internal pressure [67].…”

Section: Regressionmentioning

confidence: 99%