• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.90-93
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• 2002

In view of reliability assessment, the failure mode analysis by performance tests for machine totals is researched in this study. First, the error analysis with circular movement test data is studied. The various errors and their origins are analyzed by error equations and related parts are investigated. Second, This paper deals with analysis of vibration testing fur machine tools spindle. The various frequency components are classified by FFT and order analysis. The simple measuring devices and error evaluation programs for tests are also developed.

• Journal of KSNVE
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• v.9 no.1
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• pp.49-59
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• 1999

The mechanism of Stewart platform has many advantages for kinematic analysis and control. Thus there have been many research about employing this mechanism in the new type of machine tool. Since the vibration caused during the manufacturing process has a severely adverse effect on the machining precision. it is very important to enhance the vibrational characteristics. However. it is not easy to use finite element model for the vibration analysis. That is because the vibration behaviors of the structure vary in a complicated manner according as the length of links varies. In this paper, a Stewart platform type of machine tool is modeled in finite element method and then updated by using the experimental modal data. Finally. the static and dynamic characteristics of the finite element model are predicted and then discussed.

• Management Science and Financial Engineering
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• v.18 no.1
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• pp.49-53
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• 2012

A novel method of transient stability analysis is presented in this paper. The proposed method extracts data points near the basin-of-attraction boundary and then builds a support vector machine (SVM) model learned from the generated data. The constructed SVM classifier has been shown to reduce dramatically the conservativeness of the estimated basin of attraction.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.207-218
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• 2019

The purpose of this study is to develop a model that can systematically study the whole learning process of machine learning. Since the existing model describes the learning process with minimum coding, it can learn the progress of machine learning sequentially through the new model, and can visualize each process using the tensor flow. The new model used all of the existing model algorithms and confirmed the importance of the variables that affect the target variable, survival. The used to classification training data into training and verification, and to evaluate the performance of the model with test data. As a result of the final analysis, the ensemble techniques is the all tutorial model showed high performance, and the maximum performance of the model was improved by maximum 5.2% when compared with the existing model using. In future research, it is necessary to construct an environment in which machine learning can be learned regardless of the data preprocessing method and OS that can learn a model that is better than the existing performance.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.166-171
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• 2015

In this study, an automation tool was developed for rapid evaluation of machine tool spindle designs with automated three-dimensional finite element analysis (3D FEA) using solid elements. The tool performs FEA with the minimum data of point coordinates to define the section of the spindle shaft and bearing positions. Using object-oriented programming techniques, the tool was implemented in the programming environment of a CAD system to make use of its objects. Its modules were constructed with the objects to generate the geometric model and then to convert it into the FE model of 3D solid elements at the workbenches of the CAD system using the point data. Graphic user interfaces were developed to allow users to interact with the tool. This tool is helpful for identification of a near optimal design of the spindle based on, for example, stiffness with multiple design changes and then FEAs.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.188-188
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• 2004

Modal testing and analysis is a primary tool for obtaining reliable models to represent the dynamics of structures. When a structure is tested in order to collect measured data in modal testing, we usually use attached accelerometers to pick up the response data. Change in modal parameters due to the mass of transducers in modal testing is a well-known problem. The disadvantages are the shift of measured modal frequencies and the change of modal shapes, which can cause inaccurate results in further analysis. Modal analysis methods in frequency domain are based on a set of measured frequency response functions(FRF).(omitted)

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3563-3579
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• 2021

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.

• Smart Media Journal
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• v.6 no.2
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• pp.33-41
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• 2017

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure caused by the aging effect can significantly affect railway operations with potentially disastrous consequences, replacement detection of point machine at an appropriate time is critical. In this paper, we propose a replacement condition detection method of point machine in railway condition monitoring systems using electrical current signals, after analyzing and relabeling domestic in-field replacement data by means of OLAP(On-Line Analytical Processing) operations in the multidimensional data cube into "does-not-need-to-be replaced" and "needs-to-be-replaced" data. The system enables extracting suitable feature vectors from the incoming electrical current signals by DWT(Discrete Wavelet Transform) with reduced feature dimensions using PCA(Principal Components Analysis), and employs SVM(Support Vector Machine) for the real-time replacement detection of point machine. Experimental results with in-field replacement data including points anomalies show that the system could detect the replacement conditions of railway point machines with accuracy exceeding 98%.