• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.5
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• pp.59-65
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• 1998

In order to utilize the information of well-know grinding database or grinding machine characteristics, a database needs to be designed by considering the delicate property of the machine tools for the high precision and quality of the demanding specification. Among the machine tools for the high precision and quality of the demanding specification. Among the machine tools, machining conditions of the grinding are various and knowledge repeatance obtained form the grinding process are less credable. therefore it is desirable for database, which is used to set the grinding conditions, to utilize the maximum machine tool capability. The present paper studied on the occurance limit of chatter vibration and burn considering the characteristics of machine tool. And also basic experiments were performed to establish the optimum grinding conditions which could maximize the grinding efficiency.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.8-13
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• 1997

In order to utilize the information of well-known grinding data or grinding machine, a database needs to be designed by considering the delicate property of the machine tools for the high precision and quality of the demanding specification. Among the machine tools, machining conditions of the grinding are various and knowledge repeatance obtained form the grinding process are less credable.Therefore it is desirable for D/B, which is used to set the grinding conditions, to utilize the maximum machine tool capability. The present paper studied occurance limit of chatter vibration and burn considering the characteristics of machine tool. And also basic experiments were performed to establish optimum grinding canditions which can maximize the machining efficiency.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4607-4616
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• 2023

We implement machine learning regression models to predict peak pressures of primary and secondary systems, a major safety concern in Loss Of Condenser Vacuum (LOCV) accident. We selected the Multi-dimensional Analysis of Reactor Safety-KINS standard (MARS-KS) code to analyze the LOCV accident, and the reference plant is the Korean Optimized Power Reactor 1000MWe (OPR1000). eXtreme Gradient Boosting (XGBoost) is selected as a machine learning tool. The MARS-KS code is used to generate LOCV accident data and the data is applied to train the machine learning model. Hyperparameter optimization is performed using a simulated annealing. The randomly generated combination of initial conditions within the operating range is put into the input of the XGBoost model to predict the peak pressure. These initial conditions that cause peak pressure with MARS-KS generate the results. After such a process, the error between the predicted value and the code output is calculated. Uncertainty about the machine learning model is also calculated to verify the model accuracy. The machine learning model presented in this paper successfully identifies a combination of initial conditions that produce a more conservative peak pressure than the values calculated with existing methodologies.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.391-395
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• 2023

Helicopter accidents due to spatial disorientation in low visibility conditions continue to persist as a major issue. These incidents often stem from human error, typically induced by stress, and frequently result in fatal outcomes. This study employs machine learning to analyze flight data and evaluate the efficacy of a flight illusion detection algorithm, laying groundwork for further research. This study collected flight data from approximately 20 pilots using a simulated flight training device to construct a range of flight scenarios. These scenarios included three stages of flight: ascending, level, and descent, and were further categorized into good visibility conditions and 0-mile visibility conditions. The aim was to investigate the occurrence of flight illusions under these conditions. From the extracted data, we obtained a total of 54,000 time-series data points, sampled five times per second. These were then analyzed using a machine learning approach.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.144-149
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• 2008

This study presents the selection of cutting conditions in high speed pipe cutting machine for the better quality. A high speed pipe cutting machine which uses a rotary knife can make good quality products in short time. But, the machine is much sensitive by cutting conditions because of the complicated mechanism. In this reason, many experiments for cutting condition selection are necessary to improve quality of production. This study carried out cutting experiments with the three factors that are cutting RPM, cutting force and pooling force. 2-dimensional profile measuring instrument is used to measure which is represented by ${\Delta}h$, a sum of burr and collapse height. The effects of factors are analyzed by using MINITAB, the commercial software.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.419-427
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• 2023

In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machine learning are researched. Machine learning is one of the "statistical methods of extracting the law from data" and can be classified as supervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bed and the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defects with FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codes prepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtained through adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potential of this method is explained by conclusions.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.643-648
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• 2002

In the cutting of plate steel, the quality of the cut surfaces is strongly dependent on the cutting conditions such as cutting speed, kerf width, plate thickness, length of tip-specimen and oxygen pressure etc. the cutting tests of plate steel were carried out using CNC gas cutting machine. this paper deals with cutting characteristics of plate steel using CNC cutting machine. the width of cutting entrance and exit, the surface roughness of cutting surfaces and the cutting surface are examined at various cutting conditions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.137-143
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• 2022

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2002.05a
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• pp.159-162
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• 2002

In this paper the stress-strain curves of bearing steels at hot working conditions are obtained by compression test with a computer controlled servo-hydraulic Gleeble 3800 testing machine and elongations and reductions of area of the bearing steels are obtained by tensile test with a computer controlled servo-hydraulic Gleeble 1500 testing machine. These tests have been focused to obtain the flow stress data and optimal hot forging conditions under various conditions of strain rates and temperatures. The strain rate sensitivity exponent and reduction of area of the materials are evaluated. Experimental results are resented for various conditions of temperatures and strain rates.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.2
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• pp.431-439
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• 1996

New design principles nad necessary conditions for a mechanical system have been suggested to be kept in the design process using a washing machine transmission as a model. The necessary conditions are funcitnal requirement condition and spatial arrangement condition. The design principles to satisfy the necessary conditions are the principle of sequence and the principle of expansion. Decision sequence for state variables and design varibles of various mechanicla elements have been formulated. New automatic design program for washing machine transmission has been developed observing the necessary conditions and design principles investigated in this study. It was verified to be very effective to follow the design conditions, principles nad formulated decision sequence in mechanical system design process.