• Journal of Welding and Joining
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• 제28권6호
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• pp.45-50
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• 2010

Most of joining processes for machine and steel structure are performed by butt and fillet welding. The mechanical properties and fatigue strength of their welding zone can be effected largely by the differential of generated heat and changes of grain size according to thickness of material and number of passes in welding process. In this study, it was investigated about characteristics of fatigue failure according to thickness of material and number of passes in cruciform fillet weld zone as the basic study for safe and economic design of welding structures. Fracture modes in cruciform fillet weld zone are classified into toe failure and root failure according to non-penetrated depth. It can be accomplished economic design of welding structures considering fatigue strength when the penetrated depth in fillet weld zone is controled properly.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.88-91
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• 2001

The reliability data analysis for components of CNC machining center is studied in this paper. The failure data of mechanical part is analyzed by Exponetial, Weibull, and Log-normal distributions. And then, the optimum failure distribution model is selected by goodness of fit test. The reliability data analysis program is developed using ASP language. The failure rate, MTBF, life, and failure mode of mechanical parts are estimated and searched by this program. The failure data and analysis results are stored in the database.

• 대한기계학회논문집A
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• 제29권7호
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• pp.947-954
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• 2005

The purpose of this study is to develop failure pressure evaluation models, which are applicable to straight pipes and elbows containing an internally wall thinning defect induced by flow-accelerated-corrosion (FAC). In this study, thus, three dimensional finite element (FE) analyses are performed to investigate the dependences of failure pressure of internally wall thinned pipe on the defect shape, the pipe geometry, and the defect location and bend radius of elbow. Also, the existing failure pressure assessment models for externally wall thinned pipes are examined. Based on these, the new models for assessing failure pressure of piping components with an internally wall thinning defect are proposed. Comparison of failure pressure, predicted by proposed models, with FE analysis result shows good agreement regardless of pipe type, defect shape, and defect location and bend radius.

• 비파괴검사학회지
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• 제27권6호
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• pp.520-530
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• 2007

Combination of the parametric and the wavelet analyses of acoustic emission (AE) signals was applied to identify the failure modes in carbon fiber reinforced plastic (CFRP) composite laminates during tensile testing. AE signals detected by surface mounted lead-zirconate-titanate (PZT) and polyvinylidene fluoride (PVDF) sensors were analyzed by parametric analysis based on the time of occurrence which classifies AE signals corresponding to failure modes. The frequency band level-energy analysis can distinguish the dominant frequency band for each failure mode. It was observed that the same type of failure mechanism produced signals with different characteristics depending on the stacking sequences and the type of sensors. This indicates that the proposed method can identify the failure modes of the signals if the stacking sequences and the sensors used are known.

• 대한기계학회논문집A
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• 제30권12호
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• pp.1543-1550
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• 2006

Since the performance of joints usually determines the structural efficiency of composite structures, an extensive knowledge of the behavior of adhesive joints and the related effect on joint strength is essential for design purposes. In this study, the torque capacity of adhesive joints was predicted using the combined thermal and mechanical analyses when the adherend was a composite tube. A finite element analysis was performed to evaluate residual thermal stresses developed in the joint, and mechanical s stresses in the adhesive were calculated including both the nonlinear adhesive behavior and the behavior of composite tubes. Three different joint failure modes were considered to predict joint failure: interfacial failure, adhesive bulk failure, and adherend failure. The influence of the composite adherend stacking angle on the residual thermal stresses was investigated, and how the residual thermal stresses affect the joint strength was also discussed. Finally, the predicted results were compared with experimental results available in literature.

• 항공우주시스템공학회지
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• 제11권5호
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• pp.13-19
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• 2017

본 연구에서는 굽힘 하중을 받는 복합재료 적층판의 파손 해석을 위하여 2D 변형률 기반 파손 이론을 적용하였다. 복합재료 적층판의 비선형 기계적 거동을 모사하기 위하여, 선형 증분 접근 방식을 적용하고 단위 길이 적층판에 대한 점진적 파손 해석을 수행하였다. 크로스플라이 및 준등방성 적층 패턴에 대하여 3점 굽힘 시험을 수행하고 해석 결과와 비교 검증하였다.

• 한국기계가공학회지
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• 제20권9호
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• 한국기계가공학회지
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• 제20권12호
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Composites Research
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• 제32권5호
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• pp.258-264
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• 2019

본 논문에서는 Hashin 파손 기준식과 crack band 모델이 접목된 손상변수를 이용하여 점진적파손해석 방법이 개발되었다. 파손기준식을 이용하여 파손의 개시 유무가 판단된다. 파손이 개시된 경우에는 각 파손모드(섬유 인장/압축, 기지 인장/압축)에서 손상변수가 선형 열화 거동에 따라 계산되고, 손상강성행렬을 계산하는데 사용된다. 손상강성행렬은 손상된 재료에 반영되고, 계산된 손상강성행렬을 이용하여 재료의 완전한 파괴를 의미하는 손상변수가 1인 시점이 되기까지 점진적 파손해석이 계속해서 반복적으로 수행된다. 일련의 과정들은 상용해석프로그램인 ABAQUS에 사용자 정의 부프로그램을 이용하여 수행되었다. 제안된 점진적파손해석 도구의 검증을 위하여, 원공을 가진 복합재료 적층판의 시험 결과와 비교를 수행하였으며, 시험 중 디지털 이미지 상관법을 이용하여 획득한 변형률 거동과 해석을 통해 획득한 변형률 거동을 비교하였다. 제안된 해석결과는 시험 결과와 비교하여 유효한 일치를 보였다.

• Composites Research
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• 제34권2호
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• pp.101-107
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• 2021

복합재료를 활용하여 설계되는 구조물은 각 부품들의 조립, 체결부를 갖게 된다. 이러한 연결 또는 조인트는 구조에서 잠재적으로 취약 부분이 될 수 있다. 복합재료 볼트 조인트의 파손모드는 구조 안전성을 위해 베어링 파손모드로 설계된다. 베어링 파손모드로 파괴되는 복합재료 볼트 조인트의 하중-변위 관계는 초기 파손 발생 후 비선형 거동을 보이며, 점진적인 파손을 보인다. 이러한 비선형적이고 점진적인 복합재료 볼트 조인트의 파손거동을 정확히 예측하기 위해 본 연구에서는 기존의 파손해석 모델에서 전단 손상변수 계산 과정에 수정을 수행하였다. 수정된 파손해석 모델을 이용하여 복합재료 볼트 조인트의 베어링 응력-베어링 변형률 결과를 예측하였으며, 기존 수정되지 않은 해석모델과 비교를 통해 수정된 모델의 유효성을 입증하였다.