• Journal of the Korean Society for Precision Engineering
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• v.9 no.2
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• pp.61-68
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• 1992

To meet the requirements for accuracy, productivity and reliability of machine tools, it is necessary to evaluate the chatter-free machining performance and to improve the dynamic performance of machine tools. In order to perform dynamic design of machine tools reasonably and effectively, the joint parts must be modelled accurately because their characteristics affect significantly on the total characteristics of machine tool. In this paper, an approach which identifies the effect of joint parts on the performance of total machine tool structure was proposed. That uses the experimental modal analysis, the finite element method and the sensitivity analysis method. The effectiveness of this approach was confirmed by applying it to structures with bearing joints. And as a result of the application, the change of dynamic characteristics of bearing joints was indentified.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.208-216
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• 2003

ISO 14649 (data model for STEP-NC) is a new interface scheme or language for CAD-CAM-CNC chain under established by ISO TC184 SCI. Up to this point, the new language is mainly made for milling and turning, and other processes such as EDM will be completed in the future. Upon completion, it will be used as the international standard language for e-manufacturing paradigm by replacing the old machine-level language, so called M&G code used since 1950's. With the rich information contents included in the new language, various intelligent functions can be made by the CNC as the CNC knows what-to-make and how-to-make. In particular, On-Machine Inspection required for quality assurance in the machine level, can be done based on the information of feature­based tolerance graph. Previously, On-Machine inspection has been investigated mainly for milling operation, and only a few researches were made for turning operation without addressing the data model. In this thesis, we present a feature-based on-machine inspection process by the 4 Tasks: 1) proposing a new schema for STEP-NC data model, 2) converting the conventional tolerance scheme into that of STEP-NC, 3) modifying the tolerance graph such that the tolerance can be effectively measured by the touch probe on the machine, and 4) generating collision-free tool path for actual measurement. Task 1 is required for the incorporation of the presented method in the ISO 14649, whose current version does not much include the detailed schema for tolerance. Based on the presented schema, the tolerance represented in the conventional drafting can be changed to that of STEP-NC (Task 2). A special emphasis was given to Task 3 to make the represented tolerance accurately measurable by the touch probe on the machine even if the part setup is changed. Finally, Task 4 is converting the result of Task into the motion of touch probe. The developed schema and algorithms were illustrated by several examples including that of ISO 14649 Part 12.

• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• International Journal of Advanced Culture Technology
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• v.3 no.2
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• pp.58-66
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• 2015

This paper aims to compare the use of Centrifugal Turbine and Tesla Turbine in an application of Organic Rankine Cycle (ORC) Machine using Isopentane as working fluid expanding. The working fluid has boiling point below boiling water and works in low-temperature sources between $80-120^{\circ}C$ which can be produced from waste heat, solar-thermal energy and geothermal energy etc. The experiment on ORC machine reveals that the suitability of high pressure pump for working fluid has result on the efficiency of work. In addition, Thermodynamics theory on P-h diagram also presented the effect of heat sources' temperature and flow rate on any work. Thus, the study and design on ORC machine has to concern mainly on pressure pump, flow rate and optimized temperature. Result experiment and calculate ORC Machine using centrifugal Turbine efficiency better than Tesla turbine 30% but Tesla Turbine is cheaper and easily structure. Further study on the machine can be developed throughout the county due to its low cost and efficiency.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.84-92
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• 1997

The purpose of this study is to develop a geometric error model and path compensation algorithm for rotating axes of the 5-axis machine tools, by a method to calibrate a rotary table using one master ball and three LVDTs. It was developed a new methodology to measure 3 translation errors of the rotary table and with a compensation procedure for setup errors of the master ball. The method is experimentally verified using a ball-table and on-machine inspection method. The results showed that the geometric error models with the path compensation strategy can be practically used as a means for improving the accuracy of the machine tools with rotary table.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.47-52
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• 2002

There are many factors in machine tool error. These are thermal deformation, geometric error, machine's part assembly error, error caused by tool bending. Among them thermal error is 70% of total error of machine tool . Prediction of thermal error is very difficult. because of nonlinear tendency of machine tool deformation. In this study, we tried thermal error prediction by using multi regression analysis.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.247-256
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• 2009

In mass production industries such as steel making that have large equipment, sudden stops of production process due to machine failure can cause severe problems. To prevent such situations, machine diagnosis techniques play important roles. Many methods have been developed focusing on this subject. In this paper, we propose a method for the early detection of the failure on rotating machine, which is the most common theme in the machine failure detection field. A simplified method of calculating autocorrelation function is introduced and is utilized for ARMA model identification. Furthermore, an absolute deterioration factor such as Bicoherence is introduced. Machine diagnosis can be executed by this simplified calculation method of system parameter distance with weight. Proposed method proved to be a practical index for machine diagnosis by numerical examples.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.29-34
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• 2023

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.3
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• pp.298-303
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• 2011

Recently, demand on machine tools has been increased because the machine and automobile industry is booming. Therefore, the machine tools need to have a high accuracy and productivity. To build a high precision machine tool and increase its productivity, structural analysis needs to be carried out for vibration and stiffness of the machine tool before its detail design. However, it is the fact that many manufacturers of machine tools depend on their know-how about design experience. Therefore, in this paper, the static and dynamic analysis is carried out for evaluating a horizontal CNC lathe and then, applied to its detail design. It is positive that the analysis can lead to reduction of design time and improvement of the quality of the lathe as its design proceeds.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.149-158
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• 1994

A radial basis hybrid neural network (RHNN) is presented for on-line prediction of machine condition. A modular-based neural architecture is designed for modeling a machine condition process and for predicting future signal. A fast on-line learning algorithm is introduced. Experimental results showed the RHNN could be utilized efficiently for on-line machine condition monitoring.