# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
###### Abstract  
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. machine learning; finite element; structural health monitoring; regression; fatigue; neural network; Monte Carlo; weld; predict; metal +
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flaws is increased. Moreover, the weld toe angle, plate thicknesses, and kind of loading are additional factors that affect these constructions' fatigue lives in addition to the welding method and stresses (design and residual) [3]. Similarly, analysis indicates that faults of different kinds introduced during various stages of engineering design, production, testing, inspection, maintenance, and service are to blame for the premature beginning of fatigue cracks in components or structures [4]. Overall, these cracks can lead to assemblies or components failing due to the cyclic nature of loading that happens during operation, even if these cyclic loads are below the yielding of the base metal and filler metal [5]. Accordingly, actions are needed to effectively examine all the elements that cause fatigue damage during assembly, in service, and post-veld treatment, even though there are post-well improvement techniques such as peening, machining, and painting that can improve the weld profile, residual stress conditions, and environmental conditions of these elements or structures. Nevertheless, the approaches for the fatigue examination of welded joints are nominal stress, structural hot-spot stress, effective notch stress, fracture mechanics, and component testing, where the assessment procedure depends on data from fatigue action (e.g., stress type) and fatigue resistance (expressed as S-N curves) [6].