• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 추계학술대회 논문집
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• pp.661-662
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• 2006

• International Journal of Precision Engineering and Manufacturing
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• 제9권3호
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• pp.7-12
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• 2008

Today, manufacturers are forced to acknowledge that the life cycles of products are becoming shorter. In the case of the door trim assembly field, the highly frequent introduction of new products and the continuous increase in product varieties leads to the demand for redesigning assembly systems more often. Modular manufacturing systems can be an important issue in helping to overcome these problems. This paper presents the development of a modular assembly system for the door trim, and because it takes the change drives into consideration, this system is highly flexible in adapting to changes in the environment.

• 한국정밀공학회지
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• 제28권3호
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• pp.339-348
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• 2011

Reconfigurable manufacturing system (RMS), alternatively called changeable manufacturing, is a new manufacturing paradigm designed for rapid change in hardware and software components in order to quickly adjust production capacity and functionality in response to sudden changes in market or in regulatory requirements. Although there has been much progress in hardware components during the last decade, not much work has been done on operational issues of RMS. As one of starting studies on the operational issues, we suggest a framework for the system setup and scheduling problems to cope with the reconfigurability of RMS. System setup, which includes batching, part grouping, and loading, are concerned with the pre-arrangement of parts and tools before the system begins to process, and scheduling is the problem of allocating manufacturing resources over time to perform the operations specified by system setup. The framework consists of 8 scenarios classified by three major factors: order arrival process, part selection process, and tool magazine capacity. Each of the scenarios is explained with its subproblems and their interrelationships.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 추계학술대회 논문집
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• pp.402-404
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• 2000

The FRP pole had great advantages over other material poles. Light weight, easy installing and transporting were good characteristics. The material's superior properties represented the good durability for sea weather and air pollution, good insulation for electric, and changeable colors. In those properties, usages were like a area affected by sea, downtown, the area among the mountains and a special area for the outstanding views. It was studied that pole manufacturing method, structure analysis of pole by FEM in this study. Filament winding method was selected for a new pole manufacturing method. It produced the tapered poles and mechanically strong properties.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.355-360
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• 2000

There are many changed while a design is completed. Therefore, if the impact of the design changes is estimated, it may result in the improvement of design efficiency. But, the design changes have various types and affect other parts of the design system. Hence, it is difficult to deal with design changes directly. The purpose of this research is to develop a systematic change propagation tracing algorithm and a method of change impact analysis and then, to implement a change impact analysis system. Process based design is set up for the field of this research. Also the design, composed of design parameters and constraints, is set up for the subject of the research. Change propagation tracing algorithm traces change propagation based on the following concept : If the design parameters are changed, other parameters within the constrains including them may be changed. Using the result of change propagation tracing algorithm, changeable parameters, constraints and tasks can be found. The method of change impact analysis, to calculate change impact value from this changeable tasks, is developed. Change propagation tracing algorithm and the method of change impact analysis are implemented into change impact analysis system and it is applied to the redesign of 2 stage gear drives. It can support different kinds of design activities systematically. especially, at the redesign step, where many design change alternatives exist, change impact value of each alternative exist, change impact value of each alternative is calculated and design change is performed toward direction to minimize the impact of design change. Consequently, it is expected to improve the efficiency of the whole design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.