• Journal of the Korean Society of Safety
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• v.22 no.1 s.79
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• pp.30-35
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• 2007

With the rapid industrialization and civilization development, noise has become a major problem in cities and is a very serious issue for the environment. Noise induced in a factory has a bad influenced on operation efficiency, accuracy and detail of work. The purpose of this paper is to develop a new noise evaluation software for predicting acoustic condition including noise properties during the design of a factory. Majority of commercial softwares for this purpose have been developed in foreign countries and they are quite expensive and hard to use. A new home-made software tool has been developed in the present work, which aimed at providing a more user-friendly environment. The tool developed in this work consists of four major part; the prediction and evaluation of noise in system design, database design, noise analysis development and 3D graphic modeling. The outcome of present work is expected to provide domestic users with a more user-friendly and economic acoustic design tool.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.3-6
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• 2002

This paper presents the cutting simulation system for prediction and regulation of cutting force in CNC machining. The cutting simulation system includes geometric model, cutting force model, and off-line fred rate scheduling model. ME Z-map(Moving Edge node Z-map) is constructed for cutting configuration calculation. The cutting force models using cutting-condition-independent coefficients are developed for flat-end milling and ball-end milling. The off-line feed rate scheduling model is derived from the developed cutting force model. The scheduled feed rates are automatically added to a given set of NC code, which regulates the maximum resultant cutting force to the reference force preset by an operator. The cutting simulation system can be used as an effective tool for improvement of productivity in CNC machining.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.237-240
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• 2004

Indexable end mills, which consist of inserts and cutter body, have been widely used in roughing of parts in the mold industry. The geometry and distribution of inserts on cutter body are determined by application. This paper proposes analytical cutting force model for indexable flat end-milling process. Developed cutting force model uses the cutting-condition-independent cutting force coefficients and considers runout, cutter deflection and size effect for the accurate cutting force prediction. Unlike solid type endmill, the tool geometry of indexable endmill is variable according to the axial position due to the geometry and distribution of inserts on the cutter body. Thus, adaptive algorithm that calculates tool geometry data at arbitrary axial position was developed. Then number of flute, angular position of flute, and uncutchip thickness are calculated. Finally, presented model was validated through some experiments with aluminum workpiece.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1995.03a
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• pp.152-157
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• 1995

In Turning It is good selection of cutting condition and cutting tools that influence upon the accuracy of dimension manufacturing efficiency and extension of tool life. Among them especially the identification of cutting force due to the change of cutting conditions which exerts a great influence on the turning is very important. In this study the cutting resistance due to the change of cutting conditions was caculated by using the energy method and good agreement in shown between theoritical and experimental results which were tested for the cutting resistance at the cemented carbide cutting tools with workpieces of SM20C and SM 45C.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.313-325
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• 2022

Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10^-3, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.178-186
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• 2009

Most of automotive plastic parts are injection molding products. Inspection of total product is impossible, because number of product to inspect is too many and various. Condition-based Monitoring was proposed to decrease cost and time for inspecting. In this research, a system that predicts quality of part at fabrication point of time, and confirms informations through the internet was developed. Cavity sensors were installed inside of mold, and gathered signals as measuring, and through this process Sensor-based Monitoring system can be observed manufacturing of a part. Monitoring system transmits signals to client through the internet, and finally developed system provides manufacturing informations and predictions of quality as web-based monitoring.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.79-86
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• 2007

The shape of excessive penetration mainly depends on welding conditions(welding current and welding voltage), and welding process(groove gap and welding speed). These conditions are the major affecting factors to width and height of back bead. In this paper, back-bead prediction and weldability estimation using artificial neural network were investigated. Results are as follows. 1) If groove gap, welding current, welding voltage and welding speed will be previously determined as a welding condition, width and height of back bead can be predicted by artificial neural network system without experimental measurement. 2) From the result applied to three weld quality levels(ISO 5817), both experimented measurement using vision sensor and predicted mean values by artificial neural network showed good agreement. 3) The width and height of back bead are proportional to groove gap, welding current and welding voltage, but welding speed. is not.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.8
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• pp.53-62
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• 1995

In recent years, the rapid development of the machine tool and tough insert has made metal removal rates increase, and automatic system without human supervision requires a higher degree reliability of machining process. Therefore the control of chips is one of the important topics which deserves much attention. The chip classification was made based upon standard deviation of the mean cutting force measured by a tool dynamometer. STS304was chosen as the workpiece which is known as the difficult-to-cut material and mainly saw-toothed chip produced, and the chip type according to the standard deviation of mean cutting force was classified into five categories in this experiment. Long continuous type chip which interrupts the normal cutting process, and damages the operator, tool and workpiece has low standard deviation value, while short broken type chip, which is favourable chip for disposal, has relatively large standard deviation value. In addition, we investigated the possibility that the chip type can be predicted analyzing the relationship between chip type and cutting condition by the trained neural network, and obtained favourable results by which the chip type can be predicted with cutting conditon before cutting process.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.102-114
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• 2021

Biofouling represents an important problem in the shipping industry since it causes the increase in surface roughness. The most of ships in the current world fleet do not have good coating condition which represents an important problem due to strict rules regarding ship energy efficiency. Therefore, the importance of the control and management of the hull and propeller fouling is highlighted by the International Maritime Organization and the maintenance schedule optimization became valuable energy saving measure. For adequate implementation of this measure, the accurate prediction of the effects of biofouling on the hydrodynamic characteristics is required. Although computational fluid dynamics approach, based on the modified wall function approach, has imposed itself as one of the most promising tools for this prediction, it requires significant computational time. However, during the maintenance schedule optimization, it is important to rapidly predict the effect of biofouling on the ship hydrodynamic performance. In this paper, the effect of biofilm on the ship hydrodynamic performance is studied using the proposed performance prediction method for three merchant ships. The applicability of this method in the assessment of the effect of biofilm on the ship hydrodynamic performance is demonstrated by comparison of the obtained results using the proposed performance prediction method and computational fluid dynamics approach. The comparison has shown that the highest relative deviation is lower than 4.2% for all propulsion characteristics, lower than 1.5% for propeller rotation rate and lower than 5.2% for delivered power. Thus, a practical tool for the estimation of the effect of biofouling with lower fouling severity on the ship hydrodynamic performance is developed.