• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.332-338
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• 2000

The high speed machining is now one of the most effective manufacturing methods to achive higher productivity. However, due to the increased cutting temperatures caused by increased cutting speed, tool wear become larger. Especially in high speed face milling, cutting tools are exposed not only to high cutting temperatures, but also to mechanical and thermal shock stresses. It is essential, therefore, to know the wear characteristics of tool materials in high speed machining. This study presents an experimental investigation of the cutting performance of CBN tools in high speed face milling of gray cast iron FC25. The effect of cutting conditions and cutting length on flank wear of CBN tools and roughness of machined surfaces is investigated. The cutting parameters involved were ; cutting speeds in the range of 600to 1800 m/min, feed of 0.1 mm/tooth, and depth of cut of 0.3mm.

• 한국기계가공학회지
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• 제16권4호
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• pp.24-29
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• 2017

This paper provides an experimental analysis on the breakage of the coated tool using the face-milling cutter of the machining center due to changes in the cutting speed and the feed rate. The experimental studies were conducted using STS 304 materials and the damage to the tool was analyzed according to the change in machining time. The experiments confirmed that the cutting speed and feed rate affected the tool damage and the mechanical impact and thermal shock were determined to severely damage the tool. From the production engineering point of view, it has been experimentally investigated that the increased feed rate significantly influences the material removal rate more than the increased cutting speed.

• 한국정밀공학회지
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• 제14권11호
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• pp.163-170
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• 1997

End milling experiments are conducted to investigate characteristics of acoustic emission (AE) and cutting force singals due to tool fracture. The AE signals are obtained with a sensor attached to cutting fluid discharge nozzle. Tool states are identified with scanning electron microscopy and optical microscopy. It is demonstrated that the AE signals provide reliable informations about the cutting processes and tool states. Morever, tool fracture can be detected successfully using both the AE count rate and the standard deviation of principal cutting force.

• 한국정밀공학회지
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• 제12권12호
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• Structural Engineering and Mechanics
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• 제31권1호
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.93-98
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• 2004

A reasonable analysis of cutting force in end milling may give much advantage to improvement of productivity and cutting tool life. In order to analyze cutting force, the cutting dynamics was modelled mathematically by using chip load, cutting geometry, and the relationship between cutting forces and the chip load. Specific cutting constants of the cutting dynamics model were obtained by average cutting forces, tool diameter, cutting speed, feed, axial depth, and radial depth of cut. The effects of the cutting conditions on the specific cutting force constants in milling were studied. The model is verified through comparisons of model predicted cutting forces with measured culling forces obtained from machining experiments

• 한국생산제조학회지
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• 제18권3호
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• pp.294-299
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• 2009

Micro end-milling has been becoming an important machining process to manufacture a number of small products such as micro-devices, bio-chips, micro-patterns and so on. Many related researches have given grand effects to micro end-milling phenomenon, for example, micro end-milling mechanism, cutting force modeling and machinability. This paper strongly concerned actual problem, micro tool deflection, which causes excessive machining errors on the workpiece. Machining error were predicted and measured through a series of test micro cutting and analysis of their SEM images and FEM analysis. Experiments are carried out to validate the approaches.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1893-1896
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• 2005

Cutting force and face roughness have the largest influence on precision of a structure or processing efficiency in cutting processing. Thus cutting force model and face roughness model are necessary for this interpretation. In this paper, tool path model and face roughness model which consider the blade number of a tool and a revolution speed of tool and workpiece in the worm processing using side milling cutter are presented. This model was used to forcast the cusp. Experimental results show that the predicted cusp coincides with experimental one.

• 한국공작기계학회논문집
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• 제13권4호
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• pp.9-15
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• 2004

A reasonable analysis of cutting force in end milling may give much advantage to improvement of productivity and cutting tool life. In order to analyze cutting force, the cutting dynamics was modelled mathematically by using chip load, cutting geometry, and the relationship between cutting forces and the chip load. The specific cutting constants of the cutting dynamics model were obtained by average cutting forces, tool diameter, cutting speed, feed, axial depth, and radial depth of cut. The model is verified through comparisons of model predicted cutting forces with measured cutting forces obtained from machining experiments. The results showed good agreement and from that we could predict reasonably the cutting forces in end milling.

• 한국정밀공학회지
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• 제22권1호
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• pp.32-38
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• 2005

Generally, cutting force models use a sin function method to calculate chip thickness. In slot end milling, the error from a sin function method is much bigger than other machining because a tool rotation angle in cutting is much larger. Thus in this paper, a new method to calculate chip thickness was suggested and evaluated. In a new method, tool position data according to tool rotation are checked and stored so that it is possible correct chip thickness is calculated. Cutting force waveforms simulated from a sin function method and a new method and measured waveforms from experiments were compared and error percentages were obtained. Finally, a new method had good results for simulating cutting force in slot end milling.