• 한국전산유체공학회:학술대회논문집
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• 2008.03a
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• pp.374-377
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• 2008

The prediction of the separation trajectories of the external stores released from a military aircraft is an important task in the aircraft design area having the objective to define the operational and release envelopes. This paper presents the results obtained for store separation by employing commercial sorftwares, FLUENT and CFD-FASTRAN. FLUENT treats the rigid body motion by employing the remeshing scheme. CFD-FASTRAN uses the chimera(overset) grid and interpolations. It was found that, for the prediction of the trajectories and behavior of the stores separated from the wing, both codes shows the good agreement with the experimental results.

• 한국전산유체공학회:학술대회논문집
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• 2008.10a
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• pp.374-377
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• 2008

The prediction of the separation trajectories of the external stores released from a military aircraft is an important task in the aircraft design area having the objective to define the operational and release envelopes. This paper presents the results obtained for store separation by employing commercial sorftwares, FLUENT and CFD-FASTRAN. FLUENT treats the rigid body motion by employing the remeshing scheme. CFD-FASTRAN uses the chimera(overset) grid and interpolations. It was found that, for the prediction of the trajectories and behavior of the stores separated from the wing, both codes shows the good agreement with the experimental results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.491-496
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• 2000

In the tandem cold rolling mill, the quality is very important and requirements for thickness accuracy become more strict. Howerver, the mathematical model for prediction of rolling force was not considered an elastic deformation at the entry and delivery side of the contacted area between the worked roll and rolling strip so that where was so difficult to control of the thickness. To overcome this problem, the mathematical model included an elastic deformation of strip has been developed and applied to the field in order to predict the rolling force. The simulated results showed that the effect of elastic recovery should be included the model, even f the effect of elastic compression was not important.

• Transactions of Materials Processing
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• v.22 no.7
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• pp.371-376
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• 2013

The shape defects such as edge waves and center buckles may be formed in the rolled strip because rolling can easily produce non-homogenous elongation across the strip width. The main purpose of tension leveling is to remove such defects by eliminating the differences in elongation. In this paper, a new approach for the optimization of the process conditions in tension leveling is presented. The approach consists of an analytic model for the prediction of the strip curvature and the force at each roll. The accuracy of the proposed model is examined through comparison with the predictions from a finite element model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1062-1065
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• 2002

We have evaluated the ability of a time-delayed artificial neural network (TDANN) to predict muscle forces using only eletromyographic(EMG) signals. To achieve this goal, tendon forces and EMG signals were measured simultaneously in the gastrocnemius muscle of a dog while walking on a motor-driven treadmill. Direct measurements of tendon forces were performed using an implantable force transducer and EMG signals were recorded using surface electrodes. Under dynamic conditions, the relationship between muscle force and EMG signal is nonlinear and time-dependent. Thus, we adopted EMG amplitude estimation with adaptive smoothing window length. This approach improved the prediction ability of muscle force in the TDANN training. The experimental results indicated that dynamic tendon forces from EMG signals could be predicted using the TDANN, in vivo.

• Journal of Biomedical Engineering Research
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• v.22 no.2
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• pp.151-156
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• 2001

A computer model of the musculoskelotal system that provides accurate prediction of muscle force and body movement trom the stimulation input is desired for the effective control system design in FES. This paper aims to investigate the fundamental properties of the gradual muscle force potentiation that was not included in the previous muscle models, for future development of a model that provides vetter prediction of FES-induced muscle force and body movement. Specifically, hou the muscle length was investigated. The experimental results showed that both the force increment ratio and the time-to-peak during electrical stimulation decreased with stimulatino frequency. When the muscle potentiation state was saturated by preceding stimulation. the force did not increase any more during additive stimulation. Muscle length significantly affected the force potentiation in such a way that the force increment ratio decreased with muscle length. A new model of the muscle potentiation based on these results is desired in the future.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.46-53
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• 1999

Cutting force characteristics is closely related with tool wear on the end milling. And it is found that the tool wear can be properly obtained by observation through the tool-maker's microscope when STS 304 is cut using an end mill. The relationship between the tool wear and the cutting force is established based on data obtained from a series of experiments. A cutting force model can be derived from basic cutting force model using parasitic force components of this tool wear. The results of th simulation using the cutting force model proposed in this paper were verified experimentally and a good agreement was partly obtained. The proposed model is capable of predicting increased cutting force due to tool wear.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.6
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• pp.1003-1009
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• 2013

This paper presents a simple procedure to obtain the instantaneous cutting force constants needed to predict milling forces. Cutting force data measured in a series of slot milling tests were used to determine the cutting force constants at different feed rates. The values of the cutting force constants were determined directly at the tool rotation angle that maximized the uncut chip thickness. Then, the instantaneous cutting force constant was obtained as a function of the instantaneous uncut chip thickness. This approach can greatly enhance the accuracy of the mechanistic cutting force model for end milling. In addition, the influences of several cutting parameters on the cutting forces, such as the tool helix angle and axial depth of cut, were discussed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.485-489
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• 2004

Cutting force is important to understand cutting process in milling. To measure cutting force, tool dynamometer is widely used but it is hard to apply in workshop condition. Cutting force measurement which doesn't affect cutting process is needed. Using relations between cutting force and spindle displacement, cutting force can be predicted. Cylindrical capacitive sensor was used to measure spindle displacement during cutting. And signals from tool dynamometer collected to compare with spindle displacement. The result shows spindle displacement has a linear relation with cutting force. Using this result, a simple method to predict cutting force could be applied at workshop condition.

• Design & Manufacturing
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• v.17 no.4
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• pp.1-7
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• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.