• Structural Engineering and Mechanics
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• 제4권2호
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• pp.111-124
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• 1996

New active "intelligent" structural systems with integrated self-sensing, diagnosis, and control capabilities can lead to a new design dimension for the next generation high-performance structures and mechanical systems. However, temperature effects to the piezoelectric transducers are not fully understood. This paper is concerned with a mathematical modeling and analysis of a laminated piezothermoelastic cylindrical shell composite exposed to mechanical, electric, and thermal fields. Generic shell equations and solution procedures are derived. Contributions of spatial and time components in the mechanical, electric, and temperature excitations are discussed, and their analytical solutions derived. A laminated cylindrical shell composite with fully distributed piezoelectric layers is used in a case study; its multi-field step and impulse responses are investigated. Analyses suggest that the fully distributed actuators are insensitive to even modes due to load averaging and cancellation. Accordingly, these even modes are filtered from the total response and only the modes that are combinations of m = 1, 3, 5, ${\cdots}$ and n = 1, 3, 5, ${\cdots}$ participating in dynamic response of the shell.

• 한국기계가공학회지
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• 제6권3호
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• pp.112-118
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• 2007

Recently, the machine tools called multi-task machines, which mounted rotary axes on the table or spindle are used increasingly. Accordingly, multi-task machine tool takes a growing interest on the motion accuracy of feed drive system. In this study, measurement and diagnosis of motion errors ware attempted from circular pattern obtained by using DBB (Double ball bar) device. Those were obtained at both clockwise and counter clockwise motions in mutually perpendicularly intersecting three planes. The reliability of error measurement system for multi-task machine tool was verified by the direct test cutting.

• 한국기계가공학회지
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• 제6권3호
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• pp.31-37
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• 2007

Recently, the machine tools called multi-task machines, which mounted rotary axes on the table or spindle are used increasingly. Accordingly, multi-task machine tool takes a growing interest on the motion accuracy of feed drive system. In this study, measurement and diagnosis of motion errors ware attempted from circular pattern obtained by using DBB (Double ball bar) device. Those were obtained at both clockwise and counter clockwise motions in mutually perpendicularly intersecting three planes. The reliability of error measurement system for multi-task machine tool was verified by the direct test cutting.

• Structural Engineering and Mechanics
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• 제33권5호
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• pp.543-560
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• 2009

The rotating Rayleigh-Timoshenko beam element based on B-spline wavelet on the interval (BSWI) is constructed to discrete short shaft and stiffness disc. The crack is represented by non-dimensional linear spring using linear fracture mechanics theory. The wavelet-based finite element model of rotor system is constructed to solve the first three natural frequencies functions of normalized crack location and depth. The normalized crack location, normalized crack depth and the first three natural frequencies are then employed as the training samples to achieve the neural networks for crack diagnosis. Measured natural frequencies are served as inputs of the trained neural networks and the normalized crack location and depth can be identified. The experimental results of fatigue crack in short shaft is also given.

• 한국공작기계학회논문집
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• 제13권1호
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• pp.22-27
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• 2004

A new method that estimates the sensing ability of HH smart sensor is proposed. The new signal processing method have been developed that can distinguish among different materials relatively. The HH smart sensor was developed far recognition of materials. The HH smart sensor was made for experiment. Then, it was estimated the ability to recognize objects according to acceleration value. The sensing ability of HH smart sensor has been estimated with the $R_{SAI}$ method. Experiments and analysis were executed to estimate the ability to recognize objects according to acceleration value changing. Dynamic characteristics of HH smart sensor were evaluated relatively through a new $R_{SAI}$ method that uses the power spectrum density. Applications of this method are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.

• International Journal of Reliability and Applications
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• 제10권1호
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• pp.1-15
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• 2009

Even though the impact of manufacturing quality to reliability is not considered much as well as that of design area, a major cause of an early failure of the product is known as manufacturing problem. This research applies two different types of neural network algorithms, the Back propagation (BP) algorithm and Learning Vector Quantization (LVQ) algorithm, to identify and classify the nonrandom variation pattern on the control chart based on knowledge-based diagnosis of dimensional variation. The performance and efficiency of both algorithms are evaluated to choose the better pattern recognition system for auto body assembly process. To analyze hundred percent of the data obtained by Optical Coordinate Measurement Machine (OCMM), this research considers an application in which individual observations rather than subsample means are used. A case study for analysis of OCMM data in underbody assembly process is presented to demonstrate the proposed knowledge-based pattern recognition system.

• Industrial Engineering and Management Systems
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• 제14권4호
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• pp.392-400
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• 2015

Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.

• International Journal of Precision Engineering and Manufacturing
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• 제4권6호
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• pp.5-11
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• 2003

The number of the enterprises which utilize network technology has been increasing for solving problems such as productivity improvement, market trend analysis, and material collection for making decision. Especially, the management of equipment and the recovery time reduction when machines break down are very important factors in productivity improvement of the enterprise. Currently, most of the remote trouble diagnosis of equipment using the internet have just one function of transmitting the trouble information to the user. Therefore it does not directly reflect the user's recovery experience or the developer's new recovery methods. If the user's experienced recovery methods or the developer's recovery methods as well as the basic recovery methods are reflected online or on the internet, it makes it possible to recover faster than before. In this paper, we develop a Remote Monitoring Server (RMS) for chip mounters, and make it possible to reduce the recovery time by reflecting the user's experience and developer's new methods in addition to presenting the basic recovery methods. For this, trouble recovery concept will be defined. Based on this, many functions(trouble diagnosis, the presentation of the basic recovery methods, user's and developer's recovery method, counting function of the trouble number of each code, and presentation of usage number of each recovery methods) were developed. By utilizing the reports of the actual results of chip mounter and the notice function of the parts change time, the rate of operation of the chip mounter can be improved.

• 반도체디스플레이기술학회지
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• 제17권2호
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• pp.16-19
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• 2018

The development speed of semiconductor and display device manufacturing technology is growing faster than the development speed of process equipment. So, there is a growing need for process diagnostic technology that can measure process conditions in real time and directly. In this study, a plasma diagnosis was carried out using impedance variation due to the plasma discharge. Variation of the measurement impedance appears as a voltage change at the reference impedance, and the plasma density is calculated using this. The above experiment was conducted by integrating the plasma diagnosis system and the linear atmospheric pressure DBD plasma source. It was confirmed that plasma density varies depending on various parameters (gas flow rate, $Ar/O_2$ mixture ratio, Input power).

• 반도체디스플레이기술학회지
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• 제22권4호
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.