• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.1018-1022
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• 2000

Enhancement of the accuracy of products and productivity are essential to survive in a global industrial competition. This trend requires tighter dimensional tolerance specifications. To actively cope with the rapid change of the workpiece material and cutter geometry, a general method that can predict and analyze the machined surface is needed. Surface generation model for the prediction of the topography of machined surfaces is developed based on cutting force model considering cutter deflection and runout. This paper presents the method that constructs the three-dimensional machined surface error following the movement of a cutter, irrespective of the variations of cutting conditions. In addition, the effects of the cutting forces and the kink shape on the machined surface are extensively investigated.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.89-94
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• 2001

This paper presents a study of surface roughness prediction model by orthogonal design in turning operation. Regression analysis technique has been used to study the effects of the cutting parameters such as cutting speed, feed depth of cut, and nose radius on surface roughness. An effect of interaction between two parameters on surface roughness has also been investigated. The experiment has been conducted using coated tungsten carbide inserts without cutting fluid. The reliability of the surface roughness model as a function of the cutting parameters has been estimated. The results show that the experimental design used in turning process is a method to estimate the effects of cutting parameters on sur-face roughness.

• Transactions of Materials Processing
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• v.22 no.6
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• pp.310-316
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• 2013

Recently, numerical predictions of surface deflection based on curvature analysis have been developed. In the current study, a measure of surface deflection is proposed as the maximum variation of curvature difference between the panel and the tool in order to account for surfaces that have high curvature. The current study focused on the assessment of accuracy for the surface deflection prediction with the consideration of planar anisotropy. As an example, a shallow rectangular drawn part with rectangular embossing was considered. In terms of the proposed surface deflection measure, the maximum variation of curvature difference, the prediction with a planar anisotropic model shows better correspondence with experiment than the one using a normal anisotropic model.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.37-45
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• 2004

Grinding is a finishing operation of products in various areas. Surface roughness of industrial components obtained in grinding operation is a critical quality measure but is a function of many operating parameters and their interactions. To achieve higher surface roughness and to identify the influence of grinding parameters on surface roughness, it is an ideal situation fer using the design of experiments. This paper presents an successful optimization of grinding conditions and prediction of surface roughness using the design of experiments. From the experimental verification tests, it was observed that this approach was useful as a robust design methodology for grinding operation.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.189-198
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• 1994

The achievable machining accuracy depends upon the level of the micro-engineering, and the today's accuracy targets are dimensional tolerances in the order of 10nm and surface roughness in the order of 1nm. Such requirements cannot be satisfied by the conventional machining processes. Single point diamond turning is the one of new techniques which can produce the parts with such accuracy limits. The aims of this thesis are to get a better understanding of the complex cutting process with a diamond tool and, consequently, to develope a predicting model of a turned surface profile. In order to predict the turned surface profile, a numerical model has been developed. By means of this model, the influence of the operational settings-the material properties of the workpiece, the geometry of the cutting tool and the dynamic behaviour of the lathe-and their influences via the cutting forces upon the surface roughness have been estimated.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.2
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• pp.39-46
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• 2018

Departure Manager (DMAN) is a tool to optimize the departure sequence and to suggest appropriate take-off time and off-block time of each departure aircraft to the air traffic controllers. To that end, Variable Taxi Time (VTT), which is time duration of the aircraft from the stand to the runway, should be estimated. In this paper, a study for development of VTT prediction algorithm based on machine learning techniques is presented. The factors affecting aircraft taxi speeds were identified through the analysis of historical traffic data on the airport surface. The prediction model suggested in this study consists of several sub-models that reflect different types of surface maneuvers based on the analysis result. The prediction performance of the proposed method was evaluated using the actual operational data.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1588-1593
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• 2008

This paper describes a real-time isometric pinch force prediction algorithm from surface electromyogram (sEMG) using multilayer perceptron (MLP) for human robot interactive applications. The activities of seven muscles which are observable from surface electrodes and also related to the movements of the thumb and index finger joints were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. Four muscles were selected for the force prediction by using the Fisher linear discriminant analysis among seven muscles, and the four muscle activities provided effective information for mapping sEMG to the pinch force. The MLP structure was designed to make training efficient and to avoid both under- and over-fitting problems. The pinch force prediction algorithm was tested on five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.112 ${\pm}$ 0.082 and CORR = 0.932 ${\pm}$ 0.058. These results imply that the proposed algorithm is useful to measure the produced pinch force without force sensors in real-time. The possible applications include controlling bionic finger robot systems to overcome finger paralysis or amputation.

• Atmosphere
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• v.28 no.3
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• pp.263-272
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• 2018

The high-resolution ocean surface wind vector produced by scatterometer was assimilated within the Local Ensemble Transform Kalman Filter (LETKF) in Korea Institute of Atmospheric Prediction Systems (KIAPS). The Advanced Scatterometer (ASCAT) on Metop-A/B wind data was processed in the KIAPS Package for Observation Processing (KPOP), and a module capable of processing surface wind observation was implemented in the LETKF system. The LETKF data assimilation cycle for evaluating the performance improvement due to ASCAT observation was carried out for approximately 20 days from June through July 2017 when Typhoon Nepartak was present. As a result, we have found that the performance of ASCAT wind vector has a clear and beneficial effect on the data assimilation cycle. It has reduced analysis errors of wind, temperature, and humidity, as well as analysis errors of lower troposphere wind. Furthermore, by the assimilation of the ASCAT wind observation, the initial condition of the model described the typhoon structure more accurately and improved the typhoon track prediction skill. Therefore, we can expect the analysis field of LETKF will be improved if the Scatterometer wind observation is added.

• Atmosphere
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• v.33 no.4
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.