• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.5
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• pp.1-6
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• 2011

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. Therefore, it is very important to assess errors in machine tools. Volumetric error analysis has been developed by many researchers. This paper presents a useful technique for analyzing the volumetric errors in machine tools using the ball bar. The volumetric error model is proposed in specific vertical machining center and the program is developed for generating NC code, acquiring the ball bar data, and analyzing the volumetric errors. The developed system assesses the volumetric errors such as positional, straightness, squareness, and back lash. Also this system analyzes the dynamic performance such as servo gain mismatch. The radial data acquired by ball bar on 3D space is used for analyzing these errors. It is convenient to test the volumetric errors on 3D space because all errors are calculated at once. The developed system has been tested using an actual vertical machining center.

• International Journal of CAD/CAM
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• v.12 no.1
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• pp.1-8
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• 2012

This study analyzes the characteristics of two different kinds of squat exercise through physical experiments and a computer simulation, i.e. one with a free weight and the other with a Smith machine are studied. This study also proposes a new design for the Smith machine, which has both the advantages of each type based on the results of the analysis. The muscle force and level of stimulation of the lower extremities during squatting were calculated by running an inverse dynamics analysis program on a musculoskeletal model together with the measured motion data. The calculated results were verified by comparing with the measured EMG data. The analysis showed that squatting using free weight is more effective than squatting using the Smith machine. Meanwhile, in order to design an improved Smith machine, which is the final goal of this study, the trajectory of the barbell of the subjects during free weight squatting was measured on the sagittal plane. The measurement showed that the average slope of the trajectory of the barbell is tilted backward by $10.7^{\circ}$. Based on this measurement, this study proposes a tilted design for an improved Smith machine.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.297-304
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• 2020

In this study, the prediction method was reviewed to process a cylindrical plate forming using machine learning as a data-driven approach by roll bending equipment. The calculation of the forming variables was based on the analysis using the mechanical relationship between the material properties and the roll bending machine in the bending process. Then, by applying the finite element analysis method, the accuracy of the deformation prediction model was reviewed, and a large number data set was created to apply to machine learning using the finite element analysis model for deformation prediction. As a result of the application of the machine learning model, it was confirmed that the calculation is slightly higher than the linear regression method. Applicable results were confirmed through the machine learning method.

• Transactions of Materials Processing
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• v.32 no.2
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.3
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• pp.37-43
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• 2007

Recent technological developments in machine tools have been focused on high speed, low vibration machining and high precision machining. And the concern with multi-functional machining has been increased for the last several years. Multi-tasking machines are widely used in machine tool industries. Laser multi-tasking machine has been developed for high precision and fewer vibration machining. The purpose of this study is to evaluate the effects of structural reinforcement on Laser multi-tasking machine which is comprehensively combined turning center and laser machine. Up to date, for the structural stability evaluation of a multi-tasking machine, the analysis model has been considered only the weight of the upper parts. The positions of upper parts on multi-tasking machine have not been considered in the model. So, the results of the present FE model have revealed some difference with measurement data in case of multi-tasking machine. Design of the machine and structural analysis is carried out by FEM simulation using the commercial software CATIA V5. In the result of the structural analysis, effectiveness of reinforcement of the bed was confirmed.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.10
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• pp.113-120
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• 2006

A cam profile CNC grinding machine is developed for manufacture of high precision contoured cams. The developed machine is composed of the high precision spindle using boll bearings, the high stiffness box layer type bed and the three axis CNC controller with the high resolution AC servo motor. In this paper, structural and modal analysis for the developed machine is carried out to check the design criteria of the machine. The analysis is carried out by FEM simulation using the commercial software, CATIA V5. The machine is modeled by placing proper shell and solid finite elements. And also, this paper presents the measurement system and experimental investigation on the modal analysis of a grinding machine. The weak part of the machine is found by the experimental evaluation. The results provide structure modification data for good dynamic behaviors. And safety of the machine was confirmed by the modal analysis of modified machine design. Finally, the cam profile grinding machine was successfully developed.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.269-276
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• 2015

Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm($Na{\ddot{i}}ve$ Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.57-63
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• 2022

The purpose of this study is to provide basic data on policy development using big data analysis and machine learning algorithms as part of preparing measures to prevent child abuse. In order to analyze big data for developing machine learning algorithms to prevent child abuse, frequency analysis, related word analysis, and emotional analysis were performed after defining academic databases and social network service data as big data. related words, and emotional analysis were conducted. As a result of the study, a preventive child abuse algorithm can be developed by preparing a data collection and sharing network system to prevent child abuse from the perspective of children affected by child abuse, perpetrators, and government authorities. Although it will be possible by institutionalizing infant self-esteem, depression, and anxiety tests with clues that depression and anxiety appear due to a decrease in self-concept in the characteristics of children affected by child abuse. We suggest that continuous progress of big data collection and analysis and algorithm development research to prevent child abuse, and expects that effective policies to prevent child abuse will be realized to eradicate child abuse crimes.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.