• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.123-132
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• 2002

In this study, the static and dynamic characteristics of endmilling process was modelled and the analytic realization of chatter mechanism was discussed. In this regard, We have discussed on the comparative assessment of recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental work were performed to show the malfunctional behaviors. For this purpose, new recursive least square method (RLSM) were adopted for the on-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, The stability lobe of chatter was analysed by varying parameter of cutting dynamices in regenerative chatter mechanics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.350-354
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• 1995

The Tension Leveller of Cold Rolling Mill In POSCO performs levelling the strip in high speed line. But minor variations in operating condition of driving machines such as motor, gear box, and support bearings, a small gap-variation of supporter and strip slip by poor roll revolutions can cause serious problems in the quality of strip. In this study, firstly, A condition monitoring standard for each sensor is made through with the detail analysis of vibration and strip slip. Secondly, An automatic monitoring and diagnosing system was developed to monitor the condition of Tension Leveller, and diagnose the cause of abnormal condition. Finally, A diagnosing algorithm for abnormal condition and man-machine interface (MMI) for easy operation are developed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.971-974
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• 2001

In this study, the static and dynamic characteristics of endmilling process was modelled and the analytic realization of chatter mechanism was discussed. In this regard, We have discussed on the comparative assessment of recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental work were performed to show the malfunctional behaviors. For this purpose, new recursive(RLSM) were adopted for the on-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, the stability lobe of chatter was analysed by varying parameter of cutting dynamices in regenerative chatter mechanics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.947-951
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• 1997

Recently, the necessity of the detection of abnormal machining process is being emphasized in order to improve the machining accuracy and reduce the cost in unmanned operating system. The vibration by chatter generated in cutting processes within machine tools is a relative motion between tools and workpieces. So, if the chatter occurs, the surface roughness and accuracy of workpieces will be deteriorate and it leads to the rapid wear of tools. The author intended to use the I /sab/RMS (current of root mean square) of current sigals and the movimg C.V. (coefficient of variation) of each phase for the detection method of chatter.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.47-54
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• 2009

Spin coaters are the essential instruments in micro-fabrication processes, which apply uniform thin films to flat substrates. In this research, a spin coater diagnosis system is developed to detect the abnormal operation of TFT-LCD process in real time. To facilitate the real-time data acquisition and analysis, the circular-buffered continuous data transfer and the short-time Fourier transform are applied to the fault diagnosis system. To determine whether the system condition is normal or not, a steady-state detection algorithm and a frequency spectrum comparison algorithm using confidence interval are newly devised. Since abnormal condition of a spin coater is rarely encountered, algorithm is tested on a CD-ROM drive and the developed program is verified by a function generator. Actual threshold values for the fault detection are tuned in a spin coater in process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.6
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• pp.63-69
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• 2011

This study analyzes stress, fatigue and vibration on main rotor and body of helicopter. The maximum stress is shown on adjoint part between body and main rotor at the lower position of main rotor. As the maximum displacement amplitude is happened at 4000Hz, there is no resonance and the state of helicopter becomes safe at hovering without the abnormal air current and the disabled rotor. Among the cases of nonuniform fatigue loads, 'SAE bracket history' with the severest change of load becomes most unstable but 'Sample history' becomes most stable. In case of 'Sample History' with the average stress of 0MPa to $-10^5MPa$ and the amplitude stress of 0MPa to $8.539{\times}10^5MPa$, the possibility of maximum damage becomes 3%. This stress state can be shown with 5 times more than the damage possibility of 'SAE bracket history' or 'SAE transmission'. The structural result of this study by using the analysis of vibration and fatigue can be effectively utilized for safe and durable design of helicopter.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.45-53
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• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.1
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• pp.18-24
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• 2007

If an error occurs in a product that contains a source of vibration, an abnormal noise vibration will occur. Recently a system that has been modified from the previous method of noise detection-a method of appraising the quality of manufactured automobile part by using human ears-is being implemented in the industries of automobile parts. This new system distinguishes the product's vibration signals by measuring and analyzing the signals. Following the recent trend, it has been concluded that the appraisal process of Window Regulator Module needed an improvement. Thus, a vibration monitoring system using LabVIEW, which measures and analyzes vibration signals from a sector gear's connected part by using an accelerometer, has been developed. By analyzing the characteristics of vibration signals of both inferior and superior goods, now the quality of the product can be evaluated much more accurately.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.996-1001
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• 2006

Automatic washing machines have been improved and popularized steadily since the first electric washing machine was produced in the early 1900's. Appliance industry has tried to obtain the performance of washing machine with large capacity, high energy efficiency, low vibration and low noise levels. As the installation peace of a washer becomes closer to the living space, vibration and noise problems become more important challenges. In general, a washing machine has four legs to support its body. Four legs of the washing machine should be attached on a floor. If not so, it may cause severe vibration or walking in the spin-drying process. Unfortunately, the floor of an ordinary house is bumpy in general, and the consumers will not accept bolting washing machines to a foundation; moreover, sometimes they move the location of their washing machines to utility rooms or bath rooms or kitchens and don't care for leveling the legs exactly. In this study, we devise an auto-leg system that prevents the occurrence of abnormal vibration and walking of washing machines. It is simply composed of a spring and a friction damper. Some experiments are implemented to show the dynamic characteristics of the three-dimensional auto-legged washing machine model that is located on the even or uneven ground. A spring parameter is optimized to adjust the length of the auto-leg system automatically up to 10 mm irregularity, and the friction damper is designed to decrease a resonance induced by the spring of the auto-leg system. Some numerical results show that placing the proposed auto-leg system in a washing machine makes good performance with low vibration, as well as low noise, regardless of the unevenness of the floor.