• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1996년도 춘계학술대회 논문집
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• pp.119-123
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• 1996

The demands for robotic and automatic system are continually increasing in manufacturing fields. There were so many studies to monitor and predict system, but it were mainly relied upon measuring of cutting force, current of motor spindle and using acoustic sensor, etc. In this study digital image of time series sequence was acquired taking advantage of optical technique. Then, mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR model was selected for system model, fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, we found there was a system stability.

• 한국생산제조학회지
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• 제8권3호
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• pp.15-22
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• 1999

In this study, a machining error estimation system far vertical lathes with structural deformation and geometric errors, is realized based on the virtual manufacturing technologies. The positional and directional errors of cutting tool are determined by considering the geometric errors and dimensions of machine components and by introducing the equilibrium condition between the cutting force and structural deformation. Especially, the machining errors of vertical lathes are estimated by using the prescribed cutting test(JIS B 6331). The system can be implemented to evaluate the machining accuracies of vertical lathes at the design process and to design the high precision vertical lathes.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.1033-1037
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• 1997

In the end milling process, the information of axial and depths of cut plays an important role in adaptive control systems for precision machining and tool monitoring systems for unmanned machining. In general, it is not easy to know the depths of cut due to irregular shape of workpieces, inaccurate positioning of them on the table of machine tool and machining error in previous cutting. In addition to, even they are informed, it is difficult to match the individual position of the cutter on the varying shape of the work material. This work suggest an algorithm estimating the depths of cut based on cutting force sigal. The proposed algorithm can be applied in more extensive cutting situations, for example, presence of the tool wear, variation of work material hardness, etc.

• 한국공작기계학회논문집
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• 제10권6호
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• pp.116-125
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• 2001

The comprehensive system analysis for contour milling operation has performed in this study, which combined the each element with proper connectivity into closed loop system, and determined the system response by numerical simulation technique. The obtained simulated results were then compared with the experimental results from the practical points of view, and so forth, the governing equations were formulated into the estimation model, which predicted the total contour machining error within 25% accuracy. Through the procedural evaluation, it could ascertain the characteristics of generation mechan- ifs in circular contour machining error, and the weight of each factor.

• 한국도로학회논문집
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• 제20권2호
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• 한국정밀공학회지
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• 제28권3호
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• pp.285-291
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• 2011

The error motion of a machine tool spindle directly affects the surface errors of machined parts. The error motions of the spindle are not desired errors in the three linear direction motions and two rotating motions. Those are usually due to the imperfect of bearings, stiffness of spindle, assembly errors, external force or unbalance of rotors. The error motions of the spindle have been needed to be decreased to desired goal of spindle's performance. The level of error motion is needed to be estimated during the design and assembly process of the spindle. In this paper, the estimation method for the five degree of freedom (5 D.O.F) error motions of the spindle is suggested. To estimate the error motions of the spindle, waviness of shaft and bearings, external force model was used as input data. And, the estimation models are considering geometric relationship and force equilibrium of the five degree of the freedom. To calculate error motions of the spindle, not only imperfection of the shaft, bearings, such as rolling element bearing, hydrostatic bearing, and aerostatic bearing, but also driving elements such as worm, pulley, and direct driving motor systems, were considered.

• 한국공작기계학회논문집
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• 제17권1호
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• pp.98-105
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• 2008

In this paper, the system for monitoring process of aluminum laser welding was developed using the light signal emitted from the plasma which comes from interaction between material and laser. Photodiode for monitoring system was selected based on the spectrum analysis of light from plasma and keyhole. Behavior of plasma and keyhole was analyzed through the sensor signals. Value of sensor signal represented the light intensity and fluctuation of signal indicated the stability of plasma and keyhole. For the relation between welding condition and sensor signals, the input power and weld geometry greatly effected on the average of each sensor signals. Using the feature values of signals, estimation model for tensile strength of weld was formulated with neural network algorithm. Performance of this model was verified through coefficient of determination and average error rate.

• 한국공작기계학회논문집
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• 제18권1호
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• pp.90-102
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• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• 한국생산제조학회지
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• 제22권2호
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• pp.268-274
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• 2013

Electric discharge machining drill (EDM-drill) is an efficient process for the fabrication of micro-diameter deep metal hole. As there is non-physical contact between tool (electrode) and workpiece, EDM-drill is widely used to machine the hard machining materials such as high strength steel, cemented carbide, titanium alloys. The electro-thermal energy forces the electrode to wear out together with the workpiece to be machined. The electrode wear occurs inside of a machining hole. and It causes hard to monitor the machining state, which leads the productivity and the quality to decrease. Thus, this study presents a methodology to estimated the electrode wear amount while two coefficients (scale factor and shape factor) of the logarithmic regression model are evaluated from the experiment result. To increase the accuracy of estimation model, the linear transformation method is adopted using the differences of initial electrode wear differences. The estimation model is verified through experiment. The experimental result shows that within minute error, the estimation model is able to predict accurately.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 춘계학술대회 논문집(한국공작기계학회)
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• pp.208-213
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• 2001

Objective of this paper is to develop a new approach involving the use of an Artificial Neural Network(ANN) and multiple regression methods in the prediction of process parameters on bead height for GMA welding process. Using a series of robotic are welding, multi-pass butt welds carried out in order to verify the performance of the neural network estimator and multiple regression methods. To verify the developed system, the design parameters of the neural network estimator are selected from an estimation error analysis. The experimental results show that the proposed models can predict the bead height with reasonable accuracy and guarantee the uniform weld quality.