• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.292-292
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• 2000

Since high-power CO$_2$ Laser can be make a high densed energy to Local processing area, manufacturing processes using the laser can be processed for very Localized areas at a very fast rate with minimal or no distortion. Accordingly, the laser has been widely used in the fields of thermal manufacturing processes such as welding, fusion cutting, grooving, and heat treatment of metals. In particular, interest in the laser heat treatment process has grown tremendously in the past few years. In this process, maintaining the uniform hardening depth is important problem to obtain good quality products and to reduce heat induced distortion and residual stress. For achieving this objective, we introduced a new design technique of a fuzzy logic controller that greatly simplified the design procedure by defining several simplified design parameters. In the design procedure, the major design parameters of the controller are characterized by identifying several common aspects. From a series of simulation results, we found that the proposed design technique can be effectively used to design of a fuzzy logic controller for the LASER surface hardening process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.11
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• pp.47-56
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• 2019

A deep neural network (DNN) model was proposed to predict the upset in the inertia friction welding process using a database comprising results from a series of FEM analyses. For the database, the upset length, upset beginning time, and upset completion time were extracted from the results of the FEM analyses obtained with various of axial pressure and initial rotational speed. A total of 35 training sets were constructed to train the proposed DNN with 4 hidden layers and 512 neurons in each layer, which can relate the input parameters to the welding results. The mean of the summation of squared error between the predicted results and the true results can be constrained to within 1.0e-4 after the training. Further, the network model was tested with another 10 sets of welding input parameters and results for comparison with FEM. The test showed that the relative error of DNN was within 2.8% for the prediction of upset. The results of DNN application revealed that the model could effectively provide welding results with respect to the exactness and cost for each combination of the welding input parameters.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.1055-1058
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• 2002

Drilling process is one of the most common, yet complex operations among manufacturing processes. The performance of a drill is largely dependent upon drilling forces. Many researches focused on the effects of drill parameters on drilling forces. In this paper, the stresses occurring in drilling process are analyzed by finite element method. The analytic drilling forces considering the drill parameters and drilling conditions are used. Also, with ANSYS, geometric modeling and mesh generation of drill is performed automatically. From the study, optimal drill design and drilling condition determination may be achieved.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.2
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• pp.57-64
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• 1981

This paper develops a method of modifying the usual maximum likelihood estimators of the parameters of a two state Markov process when the trajectory of the process can only he observed at regular epochs. The method utilizes the limiting behaviors of the process and the properties of state transition counts. An efficient adaptive strategy to be used together with the modified estimator is also proposed. The properties of the new estimators and the adaptive strategy are investigated using Monte Carlo simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.210-213
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• 1995

In this study,the effect of both process parameters (wheel velocity, friction coefficients between die and billet, etc) and die-shape (abutment height and shape, flash gap, etc.) on the surface defect on forming process is theoretically investigated. For this work, computer simulation was performed by using the DEFORM, a commercial FEM code. Through numerous simulations with different parameters and die shapes, We propose one optimal die shape for CONFORM process which can remove surface defect.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.04a
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• pp.52-55
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• 2000

Investigated via a series of finite element process simulation is the effect of diverse process variables on some selected non-dimensional parameters characterizing the strip in hot strip rolling. Then on the basis of these parameters an on-line model is derived for the precise prediction of roll and roll power. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.3
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• pp.72-79
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• 2002

Though the laser cutting process is increasingly used in industry, a process automation and systematic database is still yet to be developed. In this study, as the fundamental step toward the construction of a reliable process expert system, a laser cutting quality monitoring/analysis system is developed based on simulations and experimental results. The relations between laser process parameters and laser cutting surface quality parameters such as kerf geometry, striation, surface roughness and dross formation are characterized and analyzed. A graphical user interface is used to visualize the results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2587-2593
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• 2014

Ceramic Injection Molding (CIM) is one of wide used processes in industry field and the applications are gradually being expanded to parts of medical and electric devices. In this study, the CIM process were analyzed with FEM and process parameters were studied and analyzed the effect on product quality. The shape of simple flat plate was compared to the shapes with the hole, with the round corner portion or with the side wall portion. If there are holes then the hole around the uneven density distribution and the defects such as weld lines could be occurred. The Large radius of the corners of the product give good formability and fluidity. Not only the shape parameters of product but also the process parameters during CIM are studied. The simulation results showed that the process parameters of temperature, initial fractions and velocity are important design parameters to improve the quality of products.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.653-659
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• 1994

Human experts have the various own knowledges to be applied in specialized domains. The fact that knowledge itself becomes more critical in the context of textile knowledge with rapid development of new fibers, automated equipments, processes and applications. Diversity of worsted spun yarns, lack of human expertise, and inconsistency among manually generated process plans in consequency of adjustment machine parameters owing to change up raw materials frequently increase the necessity of developing computer aided process planning(CAPP) systems for spinning process. Expert systems offer one of techniques to develop CAPP systems which would behave in a knowledgeable manner. Expert systems are the problem-solving computer program that can reach a level of performance comparable to that of a human expert in some specialized problem domain. This paper is described as job justification module. The job justification module performs to consult with users on which worsted spun yarn manufacturing process planning under the various factors, e.g., raw materials, machine parameters and required yarn counts. Also, the developed module informs the various knowledges relevant process planning. The job justification module offers the control parameters at each process and includes the various standard process plans as database. These knowledges are generated by facts and rules within rule bases.

• Nuclear Engineering and Technology
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• v.36 no.4
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• pp.338-345
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• 2004

This study examines the sensitivity of several fabrication parameters for dry process fuel, using a random sampling technique. The in-pile performance of dry process fuel with irradiation was calculated by a modified ELESTRES code, which is the CANDU fuel performance code system. The performance of the fuel rod was then analyzed using a Monte Carlo simulation to obtain the uncertainty of the major outputs, such as the fuel centerline temperature, the fission gas pressure, and the plastic strain. It was proved by statistical analysis that for both the dry process fuel and the $UO_2$ fuel, pellet density is one of the most sensitive parameters, but as for the fission gas pressure, the density of the $UO_2$ fuel exhibits insensitive behavior compared to that of the dry process fuel. The grain size of the dry process fuel is insensitive to the fission gas pressure, while the grain size of the $UO_2$ fuel is correlative to the fission gas pressure. From the calculation with a typical CANDU reactor power envelop, the centerline temperature, fission gas pressure, and plastic strain of the dry process fuel are higher than those of the $UO_2$ fuel.