• Journal of Welding and Joining
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• v.30 no.6
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• pp.80-85
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• 2012

Fatigue cracks in structural components are the most common cause of structural failure when exposed to fatigue loading. In this respect, fatigue crack detection and structural health assessment are very important. Currently, various smart materials are used for detecting fatigue crack and measurement of SIFs(Stress Intensity Factors). So, this paper presented a measurement of SIFs using MFC(Micro Fiber Composite) sensor which is the one of the smart material. MFC sensor is more flexible, durable and reliable than other smart materials. The SIFs of Mode I(K I) as well as Mode II(K II) based on the piezoelectric constitutive law and fracture mechanics are calculated. In this study, the SIF values measured by MFC sensors are compared with the theoretical results.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.96-101
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• 2001

The development of a new experimental method is required to easily observe the growth behavior of fatigue cracks. To satisfy the requirement, an image processing technique was introduced to fatigue testing. The length of surface fatigue crack could be successfully measured by the image processing system. At first, the image data of cracks were stored into the computer while the cyclic loading was interrupted. After testing, crack length was determined using image processing software which was developed by ourselves. Block matching method was applied to the detection of surface fatigue cracks. By comparing the data measured by image processing system with the data measured by manual measurement with a microscope, the effectiveness of the image processing system was established. If the proposed method is used to monitor and observe the crack growth behavior automatically, the time and efforts for fatigue test could be dramatically reduced.

• Corrosion Science and Technology
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• v.3 no.5
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• pp.222-225
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• 2004

A portable RF HTS SQUID-based susceptometer was applied to the measurement of fatigue behavior for type 316L(N) stainless steel containing 0.04% to 0.15% nitrogen content. Strain-controlled low cycle fatigue (LCF) tests were conducted at RT and $600^{\circ}C$ in air an atmosphere, and the magnetic moments were measured after the fatigue test using HTS SQUID. The magnetic moment of an as-received sample is higher than that of a fatigued sample in all the temperature ranges irrespective of the nitrogen content. The fatigue life decreased with an increasing test temperature up to $500^{\circ}C$, but increased at $600^{\circ}C$. The change of the magnetic moments by LCF test is attributed to the stress induced micro defects.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.24-27
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• 2005

The NDT(Non-Destructive Testing) is valid fur the defect detection of rolling stocks because it can be used to detect defects in invisible places. For example, in case of wheelsets fatigue cracks are initiated in the wheel seat that suffers from fretting fatigue damage. But the conventional ICFPD method can not be applied to detect such cracks in press-fit area of the axle by some technical problems. In this study, we introduced a new ICFPD (Induced Current Focusing Potential Drop) method that can be applied in press-fit area of the axle. And we performed the finite element analysis of the new ICFPD method using measured electromagnetic properties of the wheel and axle. It seems that our approach is very useful f3r the detection of defects in invisible places.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.711-720
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• 2012

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.1-8
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• 2018

Whole body fatigue detection is an important phenomenon and the factors contributing to whole body fatigue can be controlled if a mathematical model is available for its assessment. This research study aims at developing a model that categorizes whole body exertion into fatigued and non-fatigued states based on physiological and perceived variables. For this purpose, logistic regression was used to categorize the fatigued and non-fatigued subject as dichotomous variable. Normalized mean power frequency of eight muscles from 25 subjects was taken as physiological variable along with the heart rate while Borg scale ratings were taken as perceived variables. The logit function was used to develop the logistic regression model. The coefficients of all the variables were found and significance level was checked. The detection accuracy of the model for fatigued and non-fatigues subjects was 83% and 95% respectively. It was observed that the mean power frequency of anterior deltoid and the Borg scale ratings of upper and lower extremities were significant in predicting the whole body fatigued when evaluated dichotomously (p < 0.05). The findings can help in better understanding of the importance of combined physiological and perceived exertion in designing the rest breaks for workers involved in squat lifting tasks in industrial as well as health sectors.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.67-72
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• 2000

Demand for now nondestructive evaluation are growing to detect fatigue crack growth behavior to predict long term performance of materials and structure in aggressive environments especially when they are In non-visible area. Acoustic emission technique is well suited to these problems and has drawn a keen interests because of its dynamic detection ability, extreme sensitivity and location of growing defects. In this study, we analysed acoustic emission signals obtained in fatigue and tensile test of high strength fire resistance steel for frame structure with time-frequency analysis methods. The main frequency range is different in the noise and the fatigue crack propagation. It could be classified that it were also generated by composite fracture mechanics of cleavage, dimple, inclusion separation etc.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.1
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• pp.1-7
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• 2020

In general, an inspection schedule is established based on the long-term fatigue life during the design stage. However, in the design stage, it is difficult to clearly identify the uncertainty factors affecting long-term fatigue life. In this study, the probabilistic fatigue life assessment was conducted in accordance with the methodology of DNV-GL. Firstly, The initial crack distribution estimated through the initial crack propagation analysis was updated by reflecting the results of crack inspection. Secondly, the updated crack distribution was compared with the initial crack distribution, and the probability of failure was updated with the effect of crack inspection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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• pp.1001-1006
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• 2000

To verify the existing theory, non-propagating crack(NPC) does not exist in Ti which fulfills the good conditions for being of NPC, NPC detection in Ti was tried out. Also, the conception of fatigue limit in Ti and a main cause for NPC being were inquired. NPC was detected in both sharp notch root ( $\rho$=0.02mm) and micro pit (diameter = 0.25mm) which held fast to the end under stressing of fatigue limit. Therefore, the existing theory was identified as mistake. But, NPC can not be detected in smooth specimen. This fact would be due to the presumption that NPC is very small or crack does not initiate in smooth specimen. Anyway, the fatigue limit of Ti does not correspond to critical stress of crack initiation but correspond to critical stress of NPC growth. Measurement on the COD of NPC in Ti showed that the crack tip was closed even under the peak stress level at fatigue limit. But, after stress relieving annealing crack tip was opened. Consequently, compressive residual stress which is induced around the crack tip is considered to be the factor causing the NPC being.