• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.368-373
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• 2003

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analysis the performance of applied neural network, the comparison with the measured rolling force and the predicted results using two different neural networks - RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1669-1680
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• 1992

This paper deals with the prediction of cutting force and tool deflection and it's application in the flexible ball end milling process. Machining accuracy is determined by the static stiffness of tool system and the instantaneous cutting force. The static stiffness of tool system consists of the stiffness of holer and the stiffness of ball end mill. The stiffness of holder was obtained from the experimental result, and the stiffness of ball end mill with two flutes was theoretically analyzed by the finite elements method. In cutting process, the instantaneous cutting force is dependent upon the instantaneous feed and pick feed(radial depth of cut) which are varied by tool deflection. For the calculation of cutting force and deflection of ball end mill, iteration method is used with the linear interpolation to the data of cutting force obtained from rigid ball end mill and the data of tool deflection. In this paper, a for enhancing accuracy is discussed. And the selection of helix angle for minimizing machining error is also discussed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.1
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• pp.69-84
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• 2018

In order to analyze the characteristics of propeller exciting force, the hybrid grid is adopted and the numerical prediction of KCS ship model is performed for hull-propeller-rudder system by Reynolds-Averaged Navier Stokes (RANS) method and volume of fluid (VOF) model. Firstly, the numerical simulation of hydrodynamics for bare hull at oblique state is carried out. The results show that with the increasing of the drift angle, the coefficients of resistance, side force and yaw moment are constantly increasing, and the bigger the drift angle, the worse the overall uniformity of propeller disk. Then, propeller bearing force for hull-propeller-rudder system in oblique flow is calculated. It is found that the propeller thrust and torque fluctuation coefficient peak in drift angle are greater than that in straight line navigation, and the negative drift angle is greater than the positive. The fluctuation peak variation law of coefficient of side force and bending moment are different due to various causes.

• Journal of Drive and Control
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• v.20 no.4
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• pp.71-79
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• 2023

In the field of agricultural machinery, various on-field tests are conducted to measure design load for optimal design of agricultural equipment. However, field test procedures are costly and time-consuming, and there are many constraints on field soil conditions due to weather, so research on utilizing simulation to overcome these shortcomings is needed. Therefore, this study aimed to model agricultural soils using discrete element method (DEM) software. To simulate draft force, predictions are made according to travel speed and compared to field test results to validate the prediction accuracy. The measured soil properties are used for DEM modeling. In this study, the soil property measurement procedure was designed to measure the physical and mechanical properties. DEM soil model calibration was performed using a virtual vane shear test instead of the repose angle test. The DEM simulation results showed that the prediction accuracy of the draft force was within 4.8% (2.16~6.71%) when compared to the draft force measured by the field test. In addition, it was confirmed that the result was up to 72.51% more accurate than those obtained through theoretical methods for predicting draft force. This study provides useful information for the DEM soil modeling process that considers the working speed from the perspective of agricultural machinery research and it is expected to be utilized in agricultural machinery design research.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.1-8
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• 2002

Cutting force is obtained as a sum of chip removing force and ploughing force. Chip removing force is estimated by multiplying specific cutting pressure by cutting area. Since ploughing force is caused from dullness of a tool, its magnitude is constant if depth of cut is bigger than a certain value. Using the linearity of chip removing force to cutting area and the constancy of ploughing force regardless of depth of cut which is over a certain limit each force is separated from measured cutting force and used to establish cutting force model. New rotation matrix to convert the measured cutting force in reference axes into the forces in cutter axes is obtained by considering that tool angles are projected angles from cutter axes to reference axes.. Spindle tilt is also considered far the model. The predicted cutting force estimated from the model is in good agreement with the measured force.

• Safety and Health at Work
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• v.2 no.3
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• pp.236-242
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• 2011

Objectives: To determine the feasibility of predicting static and dynamic peak back-compressive forces based on (1) static back compressive force values at the lift origin and destination and (2) lifting speed. Methods: Ten male subjects performed symmetric mid-sagittal floor-to-shoulder, floor-to-waist, and waist-to-shoulder lifts at three different speeds (slow, medium, and fast), and with two different loads (light and heavy). Two-dimensional kinematics and kinetics were captured. Linear regression analyses were used to develop prediction equations, the amount of predictability, and significance for static and dynamic peak back-compressive forces based on a static origin and destination average (SODA) backcompressive force. Results: Static and dynamic peak back-compressive forces were highly predicted by the SODA, with R2 values ranging from 0.830 to 0.947. Slopes were significantly different between slow and fast lifting speeds (p < 0.05) for the dynamic peak prediction equations. The slope of the regression line for static prediction was significantly greater than one with a significant positive intercept value. Conclusion: SODA under-predict both static and dynamic peak back-compressive force values. Peak values are highly predictable and could be readily determined using back-compressive force assessments at the origin and destination of a lifting task. This could be valuable for enhancing job design and analysis in the workplace and for large-scale studies where a full analysis of each lifting task is not feasible.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.3D
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• pp.223-229
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• 2012

Skid resistance is defined as the friction between pavement surfaces and vehicle tires. Lower skid resistances were observed as the vehicle speeds the water film thicknesses were increased according to the analysis results using computer modeling. The lift force is calculated from the analysis results and depends on vehicle speeds and the water film thickness. A modified IFI(international friction index) skid resistance prediction model was developed to reduce the differences between the IFI resistance prediction model and the actual skid resistance. The correlation analysis results between the IFI prediction model and the actual skid resistance revealed that the $R^2$ using the modified IFI prediction model was 0.64 whereas the $R^2$ using the conventional IFI prediction model was 0.49. This presents the modified prediction model is better than the conventional one. An improved precise prediction model is to be obtained if water film thicknesses are considered in the modified prediction model.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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• pp.145-152
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• 1998

In this research, in order to predict the cutting force using a mechanistic model, specific cutting force was firstly obtained through the cutting experiments. Band sawing process is similar to a milling, that is multi-point cutting. Therefore it is not easy matter to evaluate specific cutting force. Thus, the thickness of workpiec was made smaller than one pitch of the saw in terms of fly cutting in the face milling process. Then the cutting force was predicted by analyzing the geometric shape of a saw tooth The tooth shape used in the research was raker set style that was generally used in band sawing. And a set of teeth is comprised of three teeth, those are ranked as left, straight and right. The mechanistic model was developed in this study considered those shapes of each tooth. From the validation experiments, the predicted cutting forces coincided well with the measured ones. Therefore the predicted cutting forces can be used for the adaptive control of saw engaging feed rate in the band sawing.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.6
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• pp.29-33
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• 2004

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analyze the performance of applied neural network the comparison with the measured rolling force and the predicted results using two different neural networks-RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.107-116
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• 2008

The excitation force of a powertrain is one of major sources for the interior noise of a vehicle. This paper presents a novel approach to predict the interior noise caused by the vibration of the power rain by using the hybrid TPA (transfer path analysis) method. Although the traditional transfer path analysis (TPA) is useful for the identification of powertrain noise sources, it is difficult to modify the structure of a powertrain by using the experimental method for the reduction of vibration and noise. In order to solve this problem, the vibration of the power rain in a vehicle is numerically analyzed by using the finite element method (FEM). The vibration of the other parts in a vehicle is investigated by using the experimental method based on vibrato-acoustic transfer function (VATF) analysis. These two methods are combined for the prediction of interior noise caused by a power rain. Throughout this research, two papers are presented. This paper presents a simulation of the excitation force of the power rain exciting the vehicle body based on numerical simulation. The other paper presents a prediction of interior noise based on the hybrid TPA, which uses the VATF of the car body and the excitation force predicted in this paper.