• Journal of Welding and Joining
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• v.28 no.6
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.7 s.238
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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.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.6
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.12 s.255
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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.

• Journal of Aerospace System Engineering
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• v.11 no.5
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• pp.13-19
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• 2017

In this study, the bending analysis of composite laminates using the classical laminated theory is investigated. A piece-wise linear incremental approach is employed to describe the nonlinear mechanical behavior of the composite laminates, and a 2D strain-based interactive failure theory is employed to predict the ultimate flexural loads. The 3-point bending tests are performed for cross-ply and quasi-isotropic laminates. The analysis results with the failure theory are verified by comparing the analysis findings to the experimental outcome.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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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.

• Structural Engineering and Mechanics
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• v.11 no.4
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• pp.407-422
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• 2001

The linear constitutive relations and the failure criteria of composite materials made of thermoviscoelastic solids are presented. The post-failure material behavior is proposed and the dynamic finite element equations are formulated. However, a nonlinear term is kept in the energy equation because it represents the effect of the second law of thermodynamics. A general purpose nonlinear three-dimensional dynamic finite element program COMPASS is upgraded and employed in this work to investigate the interdependence among stress wave propagation, stress concentration, failure progression and temperature elevation in composite materials. The consequence of truthfully incorporating the second law of thermodynamics is clearly observed: it will always cause temperature rise if there exists a dynamic mechanical process.

• Journal of Yeungnam Medical Science
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• v.40 no.2
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• pp.123-135
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• 2023

Advances in perinatal and pediatric intensive care and recent advances in mechanical ventilation during the last two decades have resulted in an exponential increase in the number of children undergoing home mechanical ventilation (HMV) treatment. Although its efficacy in chronic respiratory failure is well established, HMV in children is more complex than that in adults, and there are more considerations. This review outlines clinical considerations for HMV in children. The goal of HMV in children is not only to correct alveolar hypoventilation but also to maximize development as much as possible. The modes of ventilation and ventilator settings, including ventilation masks, tubing, circuits, humidification, and ventilator parameters, should be tailored to the patient's individual characteristics. To ensure effective HMV, education for the parent and caregiver is important. HMV continues to change the scope of treatment for chronic respiratory failure in children in that it decreases respiratory morbidity and prolongs life spans. Further studies on this topic with larger scale and systemic approach are required to ensure the better outcomes in this population.