• Journal of the Korean Institute of Gas
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• v.4 no.1 s.9
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• pp.55-62
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• 2000

A surface acoustic wave method for the evaluation of small fatigue crack initiated from a pit-type surface flaw is presented. In-situ ultrasonic experiments are performed for aluminum 2024-T3 alloy samples under the fatigue test. During the fatigue test, the surface acoustic wave reflection signal from the pit and crack is measured under different hold-stress levels. From the measured and predicted surface wave reflections the depths of fully and partially open cracks are determined and results are verified by comparing with SEM fractography The crack opening behavior of the fatigue crack is evaluated from the predicted effective crack depths. The method developed in this study can be applied to monitor and characterize crack initiation and propagation from pit-type surface flaws in the early stage of fatigue life.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1187-1194
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• 2009

During the last decade, possibility of flaw occurrences has been rapidly increased world-widely as the increase of operating times of petro-chemical facilities. For instance, from a recent in-service inspection, three different sized surface cracks were detected in welding parts of a spherical oxygen holder in Korea. While API579 code provides corresponding engineering assessment procedures to determine crack driving forces, in the present work, numerical analyses are carried out for the cracked oxygen holder to investigate effects of complex geometry, analysis model and residual stress. With regard to the detailed finite element analysis, stress intensity factors are determined from both the full three-dimensional model and equivalent plate model. Also, as an alternative, stress intensity factors are calculated for equivalent plate model by employing the noted influence stress function technique. Finally, parametric structural integrity evaluation of the cracked oxygen holder is conducted in use of failure assessment diagram method, J/T method and DPFAD method. Effects of the geometry and so forth are examined and key findings from the simulations are fully discussed, which enables to determine practical safety margins of spherical components containing a defect.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.5
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• pp.479-483
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• 2010

9% nickel alloy steels used for LNG, cryogenic liquid, storage tank are welded with dissimilar Inconel or Hastelloy welding rod and the weldment shows similar characteristic with the dissimilar metal weld of low carbon steel and austenitic stainless steel. Band type indications are sometimes shown on the film during radiography test of the weldments. Thus this study identified whether the indications are non-relevant indications through material, radiographic test, ultrasonic test, liquid penetrant test and microstructure analysis and also proposed radiography film interpretation and cause of band type indications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.53-60
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• 2020

The bogies of railway vehicles are one of the most critical components for service. Fatigue defects in the bogie can be initiated for various reasons, such as material imperfection, welding defects, and unpredictable and excessive overloads during operation. To prevent the derailment of a railway vehicle, it is necessary to evaluate and detect the defect of a connection weldment between the car body and bogie accurately. The safety of the bogie weldment was checked using an ultrasonic test, and it is necessary to determine the occurrence of defects using a learning method. Recently, studies on deep learning have been performed to identify defects with a high recognition rate with respect to a fine and similar defect. In this paper, the databases of weldment specimens with artificial defects were constructed to detect the defect of a bogie weldment. The ultrasonic inspection using the wedge angle was performed to understand the detection ability of fatigue cracks. In addition, the convolutional neural network was applied to minimize human error during the inspection. The results showed that the defects of connection weldment between the car body and bogie could be classified with more than 99.98% accuracy using CNN, and the effectiveness can be verified in the case of an inspection.