• Title/Summary/Keyword: Nondestructive Reliability Evaluation

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Evaluation of Weld Defects in Stainless Steel 316L Pipe Using Guided Wave (스테인레스 316L강의 배관용접결함에 대한 유도초음파 특성 평가)

  • Lee, Jin-Kyung;Lee, Joon-Hyun
    • Journal of the Korean Society for Nondestructive Testing
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    • v.35 no.1
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    • pp.46-51
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    • 2015
  • Stainless steel is a popular structural materials for liquid-hydrogen storage containers and piping components for transporting high-temperature fluids because of its superior material properties such as high strength and high corrosion resistance at elevated temperatures. In general, tungsten inert gas (TIG) arc welding is used for bonding stainless steel. However, it is often reported that the thermal fatigue cracks or initial defects in stainless steel after welding decreases the reliability of the material. The objective of this paper is to clarify the characteristics of ultrasonic guided wave propagation in relation to a change in the initial crack length in the welding zone of stainless steel. For this purpose, three specimens with different artificial defects of 5 mm, 10 mm, and 20 mm in stainless steel welds were prepared. By considering the thickness of s stainless steel pipe, special attention was given to both the L(0,1) mode and L(0,2) mode in this study. It was clearly found that the L(0,2) mode was more sensitive to defects than the L(0,1) mode. Based on the results of the L(0,1) and L(0,2) mode analyses, the magnitude ratio of the two modes was more effective than studying each mode when evaluating defects near the welded zone of stainless steel because of its linear relationship with the length of the artificial defect.

Detection of tension force reduction in a post-tensioning tendon using pulsed-eddy-current measurement

  • Kim, Ji-Min;Lee, Jun;Sohn, Hoon
    • Structural Engineering and Mechanics
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    • v.65 no.2
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    • pp.129-139
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    • 2018
  • Post-tensioning (PT) tendons are commonly used for the assembly of modularized concrete members, and tension is applied to the tendons during construction to facilitate the integrated behavior of the members. However, the tension in a PT tendon decreases over time due to steel corrosion and concrete creep, and consequently, the stress on the anchor head that secures the PT tendon also diminishes. This study proposes an automatic detection system to identify tension reduction in a PT tendon using pulsed-eddy-current (PEC) measurement. An eddy-current sensor is installed on the surface of the steel anchor head. The sensor creates a pulsed excitation to the driving coil and measures the resulting PEC response using the pick-up coil. The basic premise is that the tension reduction of a PT tendon results in stress reduction on the anchor head surface and a change in the PEC intensity measured by the pick-up coil. Thus, PEC measurement is used to detect the reduction of the anchor head stress and consequently the reduction of the PT tendon force below a certain threshold value. The advantages of the proposed PEC-based tension-reduction-detection (PTRD) system are (1) a low-cost (< $ 30), low-power (< 2 Watts) sensor, (2) a short inspection time (< 10 seconds), (3) high reliability and (4) the potential for embedded sensing. A 3.3 m long full-scale monostrand PT tendon was used to evaluate the performance of the proposed PTRD system. The PT tendon was tensioned to 180 kN using a custom universal tensile machine, and the tension was decreased to 0 kN at 20 kN intervals. At each tension, the PEC responses were measured, and tension reduction was successfully detected.

Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks

  • Papamarkou, Theodore;Guy, Hayley;Kroencke, Bryce;Miller, Jordan;Robinette, Preston;Schultz, Daniel;Hinkle, Jacob;Pullum, Laura;Schuman, Catherine;Renshaw, Jeremy;Chatzidakis, Stylianos
    • Nuclear Engineering and Technology
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    • v.53 no.2
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    • pp.657-665
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    • 2021
  • Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

Nondestructive Quantification of Corrosion in Cu Interconnects Using Smith Charts (스미스 차트를 이용한 구리 인터커텍트의 비파괴적 부식도 평가)

  • Minkyu Kang;Namgyeong Kim;Hyunwoo Nam;Tae Yeob Kang
    • Journal of the Microelectronics and Packaging Society
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    • v.31 no.2
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    • pp.28-35
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    • 2024
  • Corrosion inside electronic packages significantly impacts the system performance and reliability, necessitating non-destructive diagnostic techniques for system health management. This study aims to present a non-destructive method for assessing corrosion in copper interconnects using the Smith chart, a tool that integrates the magnitude and phase of complex impedance for visualization. For the experiment, specimens simulating copper transmission lines were subjected to temperature and humidity cycles according to the MIL-STD-810G standard to induce corrosion. The corrosion level of the specimen was quantitatively assessed and labeled based on color changes in the R channel. S-parameters and Smith charts with progressing corrosion stages showed unique patterns corresponding to five levels of corrosion, confirming the effectiveness of the Smith chart as a tool for corrosion assessment. Furthermore, by employing data augmentation, 4,444 Smith charts representing various corrosion levels were obtained, and artificial intelligence models were trained to output the corrosion stages of copper interconnects based on the input Smith charts. Among image classification-specialized CNN and Transformer models, the ConvNeXt model achieved the highest diagnostic performance with an accuracy of 89.4%. When diagnosing the corrosion using the Smith chart, it is possible to perform a non-destructive evaluation using electronic signals. Additionally, by integrating and visualizing signal magnitude and phase information, it is expected to perform an intuitive and noise-robust diagnosis.