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FDM에서 곡면부의 접선기울기가 제품의 표면에 미치는 영향
하만경,전재억,Ha, Man-Kyung,Jun, Jae-Uhk 한국기계가공학회 2002 한국기계가공학회지 Vol.1 No.1
Fused deposition modelling(FDM) is a rapid prototyping(RP) process that fabricates part layer by layer by deposition of molte thermoplastic material extrude from a nozzle. RP system has benefits. Benefit would be the ability to experiment with physical objects of any complexity m a relatively short period of time. But it has a matter of surface roughness and geometric accuracy. We study on Influence of tangent line angle on surface roughness at fused deposition.
하만경(Man-Kyung Ha),김상오(Sang-Oh Kim) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.6
The grinding process is dissimilar to other conventional machining methods such as turning and milling. The difference between them is due to not only the cutting edges of the grinding wheel don’t have the uniformity but also the edges act differently on the workpiece at each grinding. In spite of many researches during past decades, describing the grinding action between the grinding wheel and the workpiece was not made clearly. So statistical models and computer simulations that could overcome the variety of the cutting edges were introduced. These complexities and difficulties of illustrating the grinding process also raised an obstacle to the optimization of the grinding process and to the verification of the interrelationship between the grinding parameter and the obtained grinding result. In present paper, the finest grinding wheel for textile industry was developed by using the response surface method, genetic algorithm and Taguchi method to predict surface roughness and wear of the grinding wheel.
Machining State Diagnosis in Grinding Using the Power Signals
곽재섭(Jae-Seob Kwak),하만경(Man-Kyung Ha),구양(Yang Koo) 한국자동차공학회 2000 한국자동차공학회 지부 학술대회 논문집 Vol.- No.-
A chatter vibration and a workpiece bum are the main phenomena to be monitored in modem grinding processes. This study describes a fault diagnosis of the cylindrical plunge grinding process using the power signals. The power raw signals occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of power parameters to the chatter vibration and the grinding burn was conducted.<br/>