• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.32-40
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• 2004

This paper deals with an automated computer-aided process planning and die design system by which designer can determine operation sequences even if they have a little experience in process planning and die design for axisymmetric products. An attempt is made to link programs incorporating a number of expert design rules with the process variables obtained by commercial FEM softwares, DEFORM and ANSYS, to form a useful package. The system is composed of four main modules. The process planning and the die design modules consider several factors, such as the complexities of preform geometry, punch and die profiles, specifications of available multi former, and the availability of standard parts. They can provide a flexible process based on either the reduction in the number of forming sequences by combining the possible two processes in sequence, or the reduction of deviation of the distribution on the level of the required forming loads by controlling the forming ratios. Especially in die design module an optimal design technique and horizontal split die were investigated for determining appropriate dimensions of components of multi-former die set. It is constructed that the proposed method can be beneficial for improving the tool life of die set at practice.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.219-223
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• 2001

Within a mechanical system such as an automotive the number of standard machine parts is increasing, so that the parts selection becomes more important than ever before. Selection of appropriate bearings in the preliminary design phase of a machine is also important. In this paper, three decision-making approaches are compared to find out a model that is appropriate to bearing selection problem. An artificial neural network, which is trained with real design cases, is used to select a bearing mechanism at the first step. Then, the subtype of the bearing is selected by the weighting factor method. Finally, types of peripherals such as lubrication methods are determined by a rule-based expert system.

• Journal of the Korean Society for information Management
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• v.11 no.2
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• pp.131-164
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• 1994

This study shows the design and implementation of cataloging expert system using pattern recognition techniques. This system attemps to demonstrate the feasibility of cataloging in KORMARC format from title page and copyright page without the intervention of humans. The prototype was implemented as a rule-based system in Turbo C. To demonstrate the function and capability of the system, experimental document-group and control document-group was analyzed. The hit ratio of experimental document-group is 94%. On the other hand, the hit ratio of control document-group is 93%, a little bit lower than the experimental group.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.1
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• pp.1-13
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• 1992

The objective of this study is to develop knowledge-based system for the preliminary design and midship section design of bulk carrier and to enhance the applicability of knowledge engineering in the field of Naval Architecture. First, expert system shell called E.1 is developed in C language. E.1 supports backward-chaining, automatic iteration procedure and reiterative inference mechanism for efficient application of knowledge-based system in structural design. Knowledge representation in E.1 includes IF-THEN rules, 'facts'and 'tables'. Second, knowledge bases for the principal particulars and midship section design are developed by experimental formula, design standard and experiential knowlege. Third, hybrid system combined this knowledge-based system with the optimization program of midship section is developed. Finally, the simplified design method utilizing the regression analysis of the optimum results of stiffened plate is developed for facilitating the design process. Using this knowledge-based system, the design process and results for Bulk carrier and stiffened plates are discussed. It is concluded that knowledge-based system is efficient for preliminary design and midship section design of the ship. It is expected that the performance of the CAD system would be enhanced if the better knowledge-base is accumulated in the E.1 tool.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.3 s.246
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• pp.219-230
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• 2006

Digital CAD models are one of the most important assets the manufacturer holds. The trend toward concurrent engineering and outsourcing in the distributed development and manufacturing environment has elevated the importance of high quality CAD model and its efficient exchange. But designers have spent a great deal of their time repairing CAD model errors. Most of those poor quality models may be due to designer errors caused by poor or incorrect CAD data generation practices. In this paper, we propose a rule-based approach for healing CAD model errors. The proposed approach focuses on the design history data representation from a commercial CAD model, and the procedural method for building knowledge base to heal CAD model. Through the use of rule-based approach, a CAD model healing system can be implemented, and experiments are carried out on automobile part models.

• Korean Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.111-121
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• 1997

This study intends to develop a fixture planning module as a part of the planning system for cutting. The fixture module uses machine learning method to reuse previous failure results so that the system can reduce the repeated failures. Machine learning is one of efforts to incorporate human reasoning ability into a computerized system. A human expert designs better than a novice does because he has a wide experience in a specific area. This study implements the machine learning algorithm to have a wide experience in the fixture planning area as a human expert does. When the fixture planner finds a setup failure for the suggested operations by a process planner, it makes the process planner store its attributes and other information for the failed setup. Then the process planner applies the learned knowledge when it meets a similar case so that the planner can reduce possibility of setup failure. Also the system can teach a novice user by showing a failed setup with a modified setup.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.167-180
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• 2006

The statistical survey and analysis is much restricted to time, space and material. The statistical survey and analysis could hardly resume. The statistical survey and analysis is very important to create various and accurate information. The statistical survey and analysis which is not a expert knowledge have many problems in productivity of information, reliability and etc. In this paper, we study the design and Implementation of web-based statistical survey and analysis consulting system which a client meet easily a statistical expert on the web.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.355-365
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• 1998

The purposes of this study are to propose a network for the characterizing of the input data and to show how to design predictive neural net재가 expert system which doesn't need previous knowledge base. We derived this network from the radial basis function networks(RBFN), and named it as a statistical EBFN. The proposed network can replace the statistical methods for analyzing dynamic relations between target disease and other parameters in medical studies. We compared statistical RBFN with the probabilistic neural network(PNN) and fuzzy logic(FL). And we testified our method in the diabetes prediction and compared our method with the well-known multilayer perceptron(MLP) neural network one, and showed good performance of our network. At last, we developed the diabetes prediction expert system based on the proposed statistical RBFN without previous knowledge base. Not only the applicability of the characterizing of parameters related to diabetes and construction of the diabetes prediction expert system but also wide applicabilities has the proposed statistical RBFN to other similar problems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.178-182
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• 1995

The technique of neural networks is applied to cold forging design system. A user can select more desirable plans in cold forging design by being advised with expert's opinion from neural networks. The neural networks are learned with 3 parts which are most important in cold forging design-undercut, narrow hole, sharp corner. Using the neural networks, the cold forging design system built in this study determines forming possibility about variable shapes in product. We can get available result using the system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.79-90
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• 1995

The material handling equipment selection, that is a key task in the material handling system design, is a complex, difficult task, and requires a massive technical knowledge and systematic analysis. It is invaluable to justify the selected equipment model by the performance evaluation before its actual implementation. This paper presents an intelligent knowledge-based expert system called "IMESE" created by authors, for the selection and evaluation of material handling equipment model suitable for movement and storage of materials in a manufacturing facility. The IMESE is consisted of four modules: a knowledge base to select an appropriate equipment type, a multiple criteria decision making procedure to choose the most favorable commercial model of the selected equipment type, a database to store the list of commercial models of equipment types with their specifications, and simulators to evaluate the performance of the equipment model. The whole process of IMESE is executed under VP-Expert expert system environment.vironment.