• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.121-127
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• 2018

A system for diagnosing surface defects of long and large pipe inner overlay welds, 1m in diameter and 6m in length, was developed using a Liquid Penetrant Test (PT). First, CATIA was used to model all major units and PT machines in 3-dimensions. They were used for structural strength analysis and strain analysis, and to check the motion interference phenomenon of each unit to produce two-dimensional production drawings. Structural strength analysis and deformation analysis using the ANSYS results in a maximum equivalent stress of 44.901 MPa, which is less than the yield tensile strength of SS400 (200 MPa), a material of the PT Machine. An examination of the performance of the developed equipment revealed a maximum travel speed of 7.2 m/min., maximum rotational speed of 9 rpm, repeatable position accuracy of 1.2 mm, and inspection speed of $1.65m^2/min$. The results of the automatic PT-inspection system developed to check for surface defects, such as cracks, porosity, and undercut, were in accordance with the method of ASME SEC. V&VIII. In addition, the results of corrosion testing of the overlay weld layer in accordance with the ferric chloride fitting test by the method of ASME G48-11 indicated that the weight loss was $0.3g/m^2$, and met the specifications. Furthermore, the chemical composition of the overlay welds was analyzed according to the method described in ASTM A375-14, and all components met the specifications.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.505-510
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• 2024

Since rolling contact fatigue cracks can always occur on the rail surface, which is the contact surface between wheels and rails, railway rails require thorough inspection and diagnosis to thoroughly inspect the condition of the cracks and prevent breakage. Recent detailed guidelines on the performance evaluation of track facilities present the requirements for methods and procedures for track performance evaluation. However, diagnosing and grading rail surface damage mainly relies on external inspection (visual inspection), which inevitably relies on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we conducted a deep learning model study for rail surface defect detection using Fast R-CNN. After building a dataset of rail surface defect images, the model was tested. The performance evaluation results of the deep learning model showed that mAP was 94.9%. Because Fast R-CNN has a high crack detection effect, it is believed that using this model can efficiently identify rail surface defects.

• Imaging Science in Dentistry
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• v.47 no.2
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• pp.99-107
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• 2017

Purpose: Erosions and osteophytes are radiographic characteristics that are found in different stages of temporomandibular joint (TMJ) osteoarthritis. This study assessed the effectiveness of digital subtraction radiography (DSR) in diagnosing simulated osteophytes and erosions in the TMJ. Materials and Methods: Five intact, dry human skulls were used to assess the effectiveness of DSR in detecting osteophytes. Four cortical bone chips of varying thicknesses (0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm) were placed at the medial, central, and lateral aspects of the condyle anterior surface. Two defects of varying depth (1.0 mm and 1.5 mm) were created on the lateral, central, and medial poles of the condyles of 2 skulls to simulate erosions. Panoramic images of the condyles were acquired before and after artificially creating the changes. Digital subtraction was performed with Emago dental image archiving software. Five observers familiar with the interpretation of TMJ radiographs evaluated the images. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic accuracy of the imaging methods. Results: The area under the ROC curve (Az) value for the overall diagnostic accuracy of DSR in detecting osteophytic changes was 0.931. The Az value for the overall diagnostic accuracy of panoramic imaging was 0.695. The accuracy of DSR in detecting erosive changes was 0.854 and 0.696 for panoramic imaging. DSR was remarkably more accurate than panoramic imaging in detecting simulated osteophytic and erosive changes. Conclusion: The accuracy of panoramic imaging in detecting degenerative changes was significantly lower than the accuracy of DSR (P<.05). DSR improved the accuracy of detection using panoramic images.