• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.5
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• pp.275-281
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• 2010

Car HVAC is one of main factors influencing a potential customer's first impression. It should be fault-free, which requires the most stable control performance. So, the control algorithm consists of a proportional feedback only, not with an integral action needed for elimination of steady-state errors. To reduce the errors and make the response faster, feedforward algorithm based on predicted thermal load is added. To evaluate the performance, car HVAC is dynamically modelled and its control logic is simulated. The results shows that the proportional feedback leads to about $4^{\circ}C$ of steady-state error. When the feedback is combined with the feedforward algorithm and with a set value update based on disturbances, it predicts less than $1^{\circ}C$ of control error and improved thermal comfort.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.673-678
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• 2000

This paper presents an indirect compensation of thermal errors during machining, in which thermal error is modeled as a linear regression of temperatures measured at 4 specified positions. In this regression model, weighting coefficients of the measured temperatures were estimated by using the least square method. The grinding test with compensation, after 4-hour warming-up operation before the test, showed that the maximum machining error of the work pieces was reduced to 12${\mu}{\textrm}{m}$ while it measured 28${\mu}{\textrm}{m}$ without compensation. Furthermore the standard deviation of machining errors was also reduced from 8${\mu}{\textrm}{m}$ to 2${\mu}{\textrm}{m}$.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.1
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• pp.80-87
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• 2016

In this work, we conduct a thermal pointing error analysis of the observation satellites considering seasonal and daily temperature variation with interpolated temperature based on prescribed average temperature (PAT) method. Maximum 200 degree temperature excursion is applied to the observation satellites during on-orbit operation, which cause the line of sight (LOS) to deviate from the designated pointing direction due to thermo-elastic deformation. To predict and adjust such deviation, the thermo-elastic deformation analysis with a fine structural finite element model is accomplished with interpolated thermal maps calculated from the results of on-station thermal analysis with a coarse thermal model. After verifying the interpolated temperatures by PAT with two benchmark problems, we evaluate the thermal pointing error.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.303-308
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• 2022

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.561-571
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• 2022

An optical mirror assembly is an opto-mechanically coupled system as the optical and mechanical behaviors interact. In the assembly, a flexure mount attached to an optical mirror should be flexible in the radial direction, but rigid for the remaining degrees of freedom for supporting the mirror rigidly and suppressing the wavefront error of the optical mirror. This work presents an optimal design of the flexure mount using topology optimization with thermal stress constraint. By simplifying the optical mirror assembly into finite shell elements, topology optimization model was built for efficient design and good machinability. The stress at the boundary between the optical mirror and the mount together with the first natural frequency were applied as constraints for the optimization problem, while the objective function was set to minimize the strain energy. As a result, we obtained the optimal design of the flexure mount yielding the improved wavefront error, proper rigidity, and machinability.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.1-6
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• 2014

This paper describes a method of estimating and evaluating the volumetric errors of multi-axis machine tools. The estimation method is based on a generic model that was developed from conventional kinematic error models for the geometric and thermal errors to help predict the volumetric error easily in various configurations. To demonstrate the advantages of the model, an application in the early stages of a five-axis machine tool design is presented as an example. The model was experimentally evaluated for a four-axis machine tool by using the data from ISO230-6 and R-test measurements to compare the estimated and measured volumetric errors.

• Composites Research
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• v.34 no.6
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• pp.426-433
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• 2021

The carbon fiber reinforced plastic manufacturing process has a problem in that a dimensional error occurs due to thermal deformation such as residual stress, spring-in, and warpage. The main causes of thermal deformation are various, including the shape of the product, the chemical shrinkage, thermal expansion of the resin, and the mold effect according to the material and surface condition of the mold. In this study, a viscoelastic model was applied to the plate model to predict the thermal deformation. The effects of chemical shrinkage and thermal expansion of the resin, which are the main causes of thermal deformation, were analyzed, and the analysis technique of the 3-D viscoelastic model with and without mold was also studied. Then, the L-shaped mold effect was analyzed using the verified 3D viscoelastic model analysis technique. The results show that different residual deformation occurs depending on the surface condition even when the same mold is used.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.2
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• pp.71-76
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• 2003

A virtual machine tool (VMT) is presented in this two-part paper. In Part 1, the analytical foundation for a virtual machining system is developed, which is envisioned as the foundation for a comprehensive simulation environment capable of predicting the outcome of cutting processes. The VHT system undergoes "pseudo-real machining", before actual cutting with a CNC machine tool takes place, to provide the proper cutting conditions for process planners and to compensate or control the machining process in terms of the productivity and attributes of the products. The attributes can be characterized by the machined surface error, dimensional accuracy, roughness, integrity, and so forth. The main components of the VMT are the cutting process, application, thermal behavior, and feed drive modules. In Part 1, the cutting process module is presented. When verified experimentally, the proposed models gave significantly better prediction results than any other methods. In Part 2 of this paper, the thermal behavior and feed drive modules are developed, and the models are integrated into a comprehensive software environment.vironment.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.11
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• pp.74-79
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• 2001

In this two-part paper, a virtual machine tool (VMT) is presented. In part 1, the analytical foundation of a virtual machining system, envisioned as the foundation for a comprehensive simulation environment capable of predicting the outcome of cutting processes, is developed. The VMT system purposes to experience the pseudo-real machining before real cutting with a CNC machine tool, to provide the proper cutting conditions for process planners, and to compensate or control the machining process in terms of the productivity and attributes of products. The attributes can be characterized with the machined surface error, dimensional accuracy, roughness, integrity and so forth. The main components of the VMT are cutting process, application, thermal behavior and feed drive modules. In part 1, the cutting process module is presented. The proposed models were verified experimentally and gave significantly better prediction results than any other method. The thermal behavior and feed drive modules are developed in part 2 paper. The developed models are integrated as a comprehensive software environment in part 2 paper.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.1
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• pp.87-94
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• 2006

In this paper, we investigated a feasibility of applying highest and lowest temperatures of the next day forecasted from a meteorological observatory to operation of an air-conditioning system with thermal storage. First we investigated specific characteristics of the time series of forecasted temperatures and errors in Osaka from 1994 to 1996. Since the forecast error is not always small, it might be difficult to use the forecasted data without correction for the sizing and the control of the thermal storage system. On the other hand, the autocorrelation functions of the forecast errors decrease relatively slowly during high summer season when cooling thermal storage is required. Since the values of the autocorrelation function; for one day are larger than 0.4, not small, the forecast errors can be predicted by proper statistical analysis. Thus, the forecasted values of the highest temperatures for the next day were improved by using the stochastic time series models.