• Journal of Welding and Joining
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• 제21권3호
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• pp.92-98
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• 2003

This paper describes the numerical prediction of the thermal fatigue life of a $\mu$BGA(Micro Ball Grid Array) solder joint. Finite element analysis(FEA) was employed to simulate thermal cycling loading for solder joint reliability. Strain values, along with the result of mechanical fatigue tests for solder alloys were then used to predict the solder joint fatigue life using the Coffin-Manson equation. The results show that Sn-3.5mass%Ag solder had the longest thermal fatigue life in low cycle fatigue. Also a practical correlation for the prediction of the thermal fatigue life was suggested by using the dimensionless variable ${\gamma}$, which was possible to use several lead free solder alloys for prediction of thermal fatigue life. Furthermore, when the contact angle of the ball and chip has 50 degrees, solder joint has longest fatigue life.

• 대한기계학회논문집A
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• 제35권7호
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• pp.753-758
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• 2011

초내열합금인 GTD-111은 고온강도와 내산화성이 우수하여 가스터빈에서 사용되는 소재이다. 초내열합금의 피로 수명 예측은 가스터빈의 효율을 개선하기 위하여 매우 중요하다. 본 연구에서의 저주기 피로시험은 실제 운전 환경과 유사하게 변형률 범위, 온도를 다양하게 설정하여 시험을 수행하였다. GTD-111의 저주기 피로수명을 예측하기 위하여 변형률 에너지 밀도와 파단 사이클과의 관계를 이용하였다. 시험결과를 토대로 변형률 에너지법과 Coffin-Manson법에 의하여 피로수명을 예측하였다.

• 한국정밀공학회지
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• 제19권6호
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• pp.166-175
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• 2002

Low cycle fatigue tests are performed on the HSLA steel that be developed for a submarine material. The relation between strain energy density and numbers of cycles to failure is examined in order to predict the low cycle fatigue life of HSLA steel. The cyclic properties are determined by a least square fit techniques. The life predicted by the strain energy method is found to coincide with experimental data and results obtained from the Coffin-Manson method. Also the cyclic behavior of HSLA steel is characterized by cyclic softening with increasing number of cycle at room temperature. Especially, low cycle fatigue characteristics and microstructural changes of HSLA steel are investigated according to changing tempering temperatures. In the case of HSLA steel, the $\varepsilon$-Cu is farmed in $550^{\circ}C$ of tempering temperature and enhances the low cycle fatigue properties.

• International Journal of Naval Architecture and Ocean Engineering
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• 제11권2호
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• pp.671-678
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• 2019

In this paper, the low cycle fatigue failure and ratcheting behavior, as well as their interaction of AH32 steel were experimentally investigated under uniaxial cyclic loading. The effects of mean stress, stress amplitude and stress ratio on the low cycle fatigue life and ratcheting strain were discussed. It was found that the ratcheting strain increased while the fatigue life decreased with the increase of mean stress and stress amplitude, and the increasing stress ratio would result in smaller ratcheting and larger fatigue life. Two kinds of failure modes, i.e. low cycle fatigue failure due to crack propagates and ratcheting failure due to large plastic strain will take place respectively. Based on the experimental results, considered the effect of ratcheting on fatigue life, a model with the maximum stress and ratcheting strain rate was proposed. Comparison with the experimental result showed that the new model provided a good prediction for AH32 steel.

• 대한기계학회논문집A
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• 제29권6호
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• pp.815-821
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• 2005

Biaxial low cycle fatigue test was carried out to predict fatigue life under combined axial-torsional-loading condition which is that of in-phase and out-of-phase for CF8M cast stainless steels. Fatemi-Socie(FS) parameter which is based on critical plane approach is not only one of methods but also the best method that can predict fatigue life under biaxial loading condition. But the result showed that, biaxial fatigue life prediction by using FS parameter with several different parameters for the CF8M cast stainless steels is not conservative but best results. So in this present research, we proposed new fatigue life prediction parameter considering effective shear stress instead of FS parameter which considers the maximum normal stress acting on maximum shear strain and its effectiveness was verified.

• 한국자동차공학회논문집
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• 제18권1호
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• pp.31-36
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• 2010

Collecting all failures during life cycle of vehicle is not easy way because its life cycle is normally over 10 years. Warranty period can help gathering failures data because most customers try to repair its failures during warranty period even though small failures. This warranty data, which means failures during warranty period, can be a good resource to predict initial reliability and permanence reliability. However uncertainty regarding reliability prediction remains because this data is censored. University of Wuppertal and major auto supplier developed the reliability prognosis model considering censored data and this model introduce to predict reliability estimate further "failure candidate". This paper predicts reliability of telecommunications system in vehicle using the model and describes data structure for reliability prediction.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 춘계학술대회 논문집
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• pp.991-994
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• 2000

Recently, reliability engineering is regarded as the major field for aerospace and electronics, semiconductor related industry to improve safety and life cycle. And advanced manufacturing systems with high speed and intelligent have been developed for the betterment of machining ability In this case, reliability prediction has also important roll from design procedure to manufacturing and assembly process. Accordingly in this study, reliability evaluation system has been developed for prevention trouble. quality and life cycle improvement extremely for advanced mother machinary.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2000년도 춘계학술대회 발표논문집
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• pp.213-222
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• 2000

In present study, a stochastic model is developed for the low cycle fatigue life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. In the proposed model, fatigue phenomenon is considered as a Markov process, and damage vector and reliability are defined on every plane. Any low cycle fatigue damage evaluating method can be included in the proposed model. The model enables calculation of statistical reliability and crack initiation direction under variable multiaxial loading, which are generally not available. In present study, a critical plane method proposed by Kandil et al., maximum tensile strain range, and von Mises equivalent strain range are used to calculate fatigue damage. When the critical plane method is chosen, the effect of multiple critical planes is also included in the proposed model. Maximum tensile strain and von Mises strain methods are used for the demonstration of the generality of the proposed model. The material properties and the stochastic model parameters are obtained from uniaxial tests only. The stochastic model made of the parameters obtained from the uniaxial tests is applied to the life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. The predicted results show good accordance with experimental results.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 추계학술대회 논문집
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• pp.551-556
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• 2004

The deterioration of a shunting locomotive was characterized for the lifetime assessment. The locomotive has been used for shunting works in steel making processes, and in this investigation, various types of technical evaluation methods for the locomotive parts were employed to assess the current deterioration status and to provide important clue for lifetime prediction. Unlike other rolling stocks in railway applications, the diesel shunting locomotive is composed of major components such as diesel engine, transmission, gear box, brake system, electronic devices, etc., which cover more than 70 percent of the total price of the locomotive. Therefore, in this paper, each part of major components in the diesel locomotive was analyzed in terms of the degree of deterioration. The life-cycle-cost (LCC) analysis was performed based on the maintenance and repair history as compared with economical cost to provide the cost-effective prediction, i.e., to assess either repair for reuse or putting the locomotive out of service based on cost-effective calculation.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.