• 산업경영시스템학회지
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• 제16권27호
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• pp.73-82
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• 1993

In this paper, We determine the expected travel time for several forks Storage/Retrieval machine which is allowed multiple stops in aisle. When throughput is increased, We propose adding to fork number of each S/R machine rather than adding to number of S/R machine, We also describe such a model which determines the optimal number of each several forks S/R machine subject to constraints on the hourly throughput and warehouse dimensions. Numerical example is presented to compare warehouse shapes against each single fork, twin forks, triple forks S/R machine for various throughput values.

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.305-310
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• 1997

In this paper, a fuzzy set based machine-cell formation algorithm for cellular manufacturing is presented. The fuzzy logic is emoloyed to express the degree of appropriateness when alternative machines are specified to process a part shape. For machine grouping, the similarity coefficient based approach is used. The algorithm produces efficient machine cells and part families which maximize the similarity values.

• International Journal of Precision Engineering and Manufacturing
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• 제4권4호
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• pp.57-63
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• 2003

Machining information such as form accuracy and surface roughness is an important factor for manufacturing precise parts. To this regard, OMM (On the Machine Measurement) has been researched for last several decades to alternate CMM (Coordinate Measurement Machine) process. In this research, the OMM system with a laser displacement sensor was developed for measuring form accuracy and surface roughness of the machined workpiece on the machine tool. The surface roughness was estimated comparing the sensory signal with the reference data measured from master specimen. Also, form accuracy was determined from the moving averaged raw data. In addition, the geometric error map constructed beforehand using the geometric errors of the machine tool was used to compensate the obtained form accuracy. The overall performance was compared with CMM result, and verified the feasibility of the measurement system.

• 한국해양공학회지
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• 제36권3호
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• 한국정밀공학회지
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• 제13권10호
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• pp.71-77
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• 1996

This paper describes the development of on the machine measuring(OMM) system which can directlry measure the three dimensional machined dimensilnal accuracy using scanning probe in milling machine. Two algolithms, continuous path(CP) measurement using UC program and CAD data assisted point to point(PTP) measurement, were developed regarding specification of scanning probe. The OMM system was contructed to verify the developed system suing the proposed algorithm, and actually measured three kinds of machined TV shadow mask molds. The developed system was evaluated it's repeatability and compared with the current measurement system of CMM(Coording Measuring Machine) in terms of relative accuracy and time reduction and productivity increase.

• 한국정밀공학회지
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• 제27권3호
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• pp.27-32
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• 2010

The OMM(On-Machine-Measurement) system has many advantages compare to conventional measurement in the way the time and cost. But, the sensor suitable to OMM system is restrictive use. Touch trigger probe sensor has long time for measurement and non-contact sensor has directional demerit. Because the long mechanical parts such as gear and lead screw for pump, injector and machine tools has big and heavy, unclamp and transferring for measurement in machining process is very difficult. This paper presents a development of ultra miniature eddy current displacement sensor with 3 axes for On-Machine-Measurement system. The accuracy of the sensor is experimentally proved in the grinding machine. In experimental results, the accuracy has under ${\pm}5\;{\mu}m$.

• 대한기계학회논문집A
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• 제28권6호
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• pp.867-875
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• 2004

In order to inspect burr geometry and hole quality in micro-drilling processes, a cost-effective method using an image processing and shape from focus (SFF) methods on the machine tool is proposed. A CCD camera with a zoom lens and a novel illumination unit is used in this paper. Since the on-machine vision unit is incorporated with the CNC function of the machine tool, direct measurement and condition monitoring of micro-drilling processes are conducted between drilling processes on the machine tool. Stainless steel and hardened tool steel are used as specimens, as well as twist drills made of carbide are used in experiments. Validity of the developed system is confirmed through experiments.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.137-140
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• 2003

Recently, as the demand of small-sized precision parts is rising in precision industry such as mobile, automobile, optic. etc. the requirement of small-sized machine tool is increasing. Desktop machine tool define small-sized machine tool that is able to install in table. According to diminishing in size, Desktop machine tool is able to economize production cost by reducing work area and consuming electric power. But Desktop machine tool generates vibration in acceleration and deceleration modes by inertia force of moving part. Also vibration is generated when it move simultaneously two axis or three axis. Such generating vibration situation is reason of declining stiffness of machine tool structure because of smallizing in size. And this vibration has a large effect on precision of machining products. Therefore, evaluating of the control characteristic is necessary for minimizing vibration of machine tool as much as possible to accomplish precision machining of small-sized parts

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.303-308
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• 2018

Demand is increasing rapidly in recent years than supply to machine learning professionals. To alleviate this gap, user-friendly machine learning software that can be used by non-specialists has emerged, which is Machine Learning-as-a-Service(MLaaS). MLaaS provides services that enable businesses to easily leverage ML capabilities without expertise. In this paper, we will compare and analyze features, interfaces, supporting programming language, ML framework, and Machine Learning services of MLaaS, to help companies easily use ML service.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.