• Journal of Mechanical Science and Technology
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• 제20권10호
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• pp.1534-1540
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• 2006

Concurrent Engineering (CE) is a popular method employed in product development. It treats the whole product design process by the consideration of product quality, cost, rate of progress, and demands of customers. The development of computer and network technologies provides a strong support to the realization of CE in practice. Aiming at the characteristics of CE and network collaborative design, this paper built network collaborative design system frame. Through the analysis of the network collaborative design modes based on CE, this paper provided a novel network collaborative design integration model. This model can integrate the product design information, design process, and knowledge. Intelligent collaboration was considered in the proposed model. The study showed that the proposed model considered main factors such as information, knowledge, and design process in collaborative design. It has potential application in CE fields.

• 조명전기설비학회논문지
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• 제29권8호
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• pp.18-22
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• 2015

Digitalization of the substation in Korea has been in progress so far with focusing on Station Bus. However, its application to Process Bus has been delayed due to some technical issues. IEDs based on Process Bus use the data values of SV and GOOSE. As the size of communication data on Process Bus is comparatively bigger than that of Station Bus, it is very important for the evaluating the performance of Process Bus to analyze the transferring speed and quality of data from the first equipment, which is located on process level, to station level. According to the results of related studies, it is said that the most important factor for the design and operation of Process Bus network is the communication delay with consideration of the volume of packets. In the paper, the results of performance test for the network with and without application of VLAN on Process Bus system that uses integrated IEDs are presented. Additionally, the paper proposes the optimal method to analyze the communication delays of network systems through evaluating the maximum delay time, link process ratio and the amount of lost packets by using a simulation tool.

• 대한산업공학회지
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• 제39권5호
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• 제어로봇시스템학회논문지
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• 제19권6호
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• pp.563-568
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• 2013

The purpose of this study is to find the analytic solution for determining the optimal capacity (lot-size) of a batch-storage network to meet the finished product demand under infrequent shutdowns. Batch processes are bound to experience random but infrequent operating time losses. Two common remedies for these failures are duplicating another process or increasing the process and storage capacity, both of which are very costly in modern manufacturing systems. An optimization model minimizing the total cost composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units is pursued with the framework of a batch-storage network of which flows are susceptible to infrequent shutdowns. The superstructure of the plant consists of a network of serially and/or parallel interlinked batch processes and storage units. The processes transform a set of feedstock materials into another set of products with constant conversion factors.A novel production and inventory analysis method, the PSW (Periodic Square Wave) model, is applied. The advantage of the PSW model stems from the fact it provides a set of simple analytic solutions in spite of a realistic description of the material flow between processes and storage units. The resulting simple analytic solution can greatly enhance a proper and quick investment decision at the early plant design stagewhen confronted with diverse economic situations.

• 대한전기학회논문지
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• 제45권1호
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• 인터넷정보학회논문지
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• 제20권2호
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• pp.101-108
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• 2019

Workflow management systems (WfMSs) automate and manage workflows, which are implementations of organizational processes operated in process-centric organizations. In this paper, wepropose an algorithm to discover temporal work transference networks from workflow execution logs. The temporal work transference network is a special type of enterprise social networks that consists of workflow performers, and relationships among them that are formed by work transferences between performers who are responsible in performing precedent and succeeding activities in a workflow process. In terms of analysis, the temporal work transference network is an analytical property that has significant value to be analyzed to discover organizational knowledge for human resource management and related decision-making steps for process-centric organizations. Also, the beginning point of implementinga human-centered workflow intelligence framework dealing with work transference networks is to develop an algorithm for discovering temporal work transference cases on workflow execution logs. To this end, we first formalize a concept of temporal work transference network, and next, we present a discovery algorithm which is for the construction of temporal work transference network from workflow execution logs. Then, as a verification of the proposed algorithm, we apply the algorithm to an XES-formatted log dataset that was released by the process mining research group and finally summarize the discovery result.

• 소성∙가공
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• 제8권4호
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• pp.331-339
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• 1999

In this study, to analyze the shaped drawing process from round bar, the practical conical die with considering die radius and bearing was defined by a mathematical expression, and also a simple technique for initial mesh generation to the shaped drawing process was proposed. The drawing of rectangular section from round bar, one of the shaped drawing process, has been simulated by using non-steady state 3D rigid plastic finite element method in order to evaluate the influence of semi-die angle and reduction in area to corner filling. Other process variables such as friction constant, rectangular ratio, die radius and bearing length were fixed during the simulation. An artificial neural network has been introduced to obtain the optimal process conditions which gave rise to a fast simulation.

• 한국공작기계학회논문집
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• 제11권4호
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• pp.89-96
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• 2002

This paper aims to develop an intelligent model for predicting top-bead width for the robotic GMA(Gas Metal Arc) welding process using BP(Back-propagation) neural network and multiple regression analysis. Firstly, based on experimental data, the basic factors affecting top-bead width are identified. Then BP neural network model and multiple regression models of top-bead width are established. The modeling methods and procedure are explained. The developed models are then verified by data obtained from the additional experiment and the predictive behaviors of the two kind of models are compared and analysed. Finally the modeling methods, predictive behaviors md the advantages of each models are discussed.

• 한국정밀공학회지
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• 제13권2호
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• pp.37-47
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• 1996

This paper presents an automatic tele-inspection scheme for the remotely manufacturing process. The remote-manufacturing process is continuously monitored and a crucial process is captured by CCD Camera. The captured image is compressed by neural network and JPEG, and it is sent directly to the assembly plant for incoming inspection. Massive image data require broadband channel to transmit them to remote distance, but sender is able to transmit them to receiver in use common channel by compressing massive image data in the high ratio. After the receiver reconstructs the compressed image to be transmitted, the reconstructed image is also directly used for automatic inspection of the process. The Experimental results show that the proposed inspection mechanism could be effectively implemented for real applications.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.252-258
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• 1995

The purpose of this study is to provide the integrated process control system, utilizing neural network modeling, to search for the appropriate choice input, and to keep the process output within the desired rang in the real etch process.