• 한국정밀공학회지
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• 제16권12호
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• pp.54-59
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• 1999

High precision internal plunge grinding is difficult because of the decrease in the quill stiffness due to the small diameter of wheel. In this paper, the characteristics of internal plunge grinding were investigated. Grinding experiments were performed at various grinding conditions with vitrified bonded CBN wheels. The grinding period was assumed to be consisted of rough grinding and fine grinding. The classification of grinding was determined int terms of the normal grinding forces and actual depth of cut. The experimental results indicate that the higher depth of cut and infeed speed result in the longer rough grinding time. The maximum normal grinding force was nearly equal to the static force and it decreases exponentially as the grinding continues.

• 한국기계가공학회지
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• 제15권1호
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• pp.68-73
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• 2016

In this paper, the grinding characteristics in internal grinding methods(plunge, thrust) were studied with vitreous CBN wheels using machining center. Grinding experiments were performed according to the same material removal rate conditions such as a wheel speed, depth of cut and workpiece speed. And the grinding force, machining error and grinding ratio were investigated though these experiments. Based on the experimental results, the grinding characteristics on internal grinding methods were compared.

• 한국생산제조학회지
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• 제6권1호
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• pp.34-44
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• 1997

This study presents the recursive constraint bounding(RCB) method to reduce the cycle time in internal cylindrical plunge grinding process. This method can cope with process noise as well as modeling bias. The main features of RCB method are its utilization of measurements at the end of each cycle and its use of monotonicity analysis for determining the extremes of bias and noise. This method is investigated in simulation and evaluated by experiment in internal cylindrical plunge grinding operation. The results from simulation and experiment show that it is effective in reducing cycle time in spite of modeling uncertainty in the forms of process noise and modeling bias.