• 한국정밀공학회지
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• 제16권12호
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• pp.46-53
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• 1999

Cutting force characteristics is closely related with tool wear on the end milling. And it is found that the tool wear can be properly obtained by observation through the tool-maker's microscope when STS 304 is cut using an end mill. The relationship between the tool wear and the cutting force is established based on data obtained from a series of experiments. A cutting force model can be derived from basic cutting force model using parasitic force components of this tool wear. The results of th simulation using the cutting force model proposed in this paper were verified experimentally and a good agreement was partly obtained. The proposed model is capable of predicting increased cutting force due to tool wear.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.1003-1008
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• 1997

In this paper, the prediction of cutting force and tool deflection in the ball-end milling process are studied. Identifying various cutting region using Z-map, cutting force in the ball-end milling process can be predicted. Cutting force deflects the tool and the tool deflection changes the cutting force. Tool deflection is included in the cutting force prediction. Tool deflecition also causes machining error of the machined surface. A series of experiments were performed to verify the simulated cutting force and machining error.

• 한국정밀공학회지
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• 제20권8호
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• pp.39-46
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• 2003

Productivity and machining performance can be improved by cutting analysis including cutting force prediction, surface error prediction and machining stability evaluation. In order to perform cutting analysis, cutting force coefficients database have to be constructed. Since cutting force coefficients are dependent on cutting condition in the existing research, a large number of calibration tests are needed to obtain cutting force coefficients, which makes it difficult to build the cutting force coefficients database. This paper proposes a generalized method for constructing the cutting force coefficients database us ins cutting-condition-independent coefficients. The tool geometry and workpiece material were considered as important components for database construction. Cutting force coefficients were calculated and analyzed for various helix and rake angles as well as for several workpiece. Furthermore, the variation of cutting force coefficients according to tool wear was analyzed. Tool wear was found to affect tool geometry, which results in the change of cutting force coefficients.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2000년도 제31회 춘계학술대회
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• pp.68-75
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• 2000

The development of flexible automation in the manufacturing industry is concerned with production activities performed by unmanned machining system. A major topic relevant to metal-cutting operations is monitoring tool wear, which affects process efficiency and product quality, and implementing automatic tool replacements. In this paper, the measurement of the cutting force components has been found to provide a method for an in-process detection of tool wear. Cutting force components are divided into static and dynamic components in this paper, and the static components of cutting force have been used to detect flank wear. To eliminate the influence of variations in cutting conditions, tools, and workpiece materials, the force modeling is performed for various cutting conditions. The normalized force disparities are defined in this paper, and the relationships between normalized disparity and flank wear are established. Finally, Artificial neural network is used to learn these relationships and detect tool wear. According to the proposed method, the static force components could provide the effective means to detect flank wear for varying cutting conditions in turning operation.

• 한국정밀공학회지
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• 제12권3호
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• pp.130-143
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• 1995

This paper presents a new methodology for on-line tool breadage detection by sensor fusion of an acoustic emission (AE) sensor and a built-in force sensor. A built-in piezoelectric force sensor, instead of a tool dynamometer, was used to measure the cutting force without altering the machine tool dynamics. The sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A sighificant drop of cutting force was utilized to detect tool breakage. The algorithm was implemented on a DSP board for in-process tool breakage detection. Experiental works showed an excellent monitoring capability of the proposed tool breakage detection system.

• 한국산업융합학회 논문집
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• 제5권4호
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• pp.373-378
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• 2002

This paper presents a method to predict tool deflection induced by the thermal distribution and the cutting force using FEM in milling operation. The thermal distribution of cutting tool was predicted using FEM after measuring the temperature of the end of tool and of the tool holder. The thermal deflection of cutting tool was predicted using FEM as well. The tool deflection induced by the cutting force was analyzed with the solid model of cutting tool. An end mill tool caused most of tool deflection comparing to tool holder. Most of thermal deflection came from Z-direction and most of tool deflection induced by the cutting force came from X and Y direction. Precision cutting will be accomplished when tool locations are generated considering the thermal deflection of cutting tool and the tool deflection induced by the cutting force in CAD/CAM.

• 한국공작기계학회논문집
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• 제11권2호
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• pp.1-8
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• 2002

Cutting force is obtained as a sum of chip removing force and ploughing force. Chip removing force is estimated by multiplying specific cutting pressure by cutting area. Since ploughing force is caused from dullness of a tool, its magnitude is constant if depth of cut is bigger than a certain value. Using the linearity of chip removing force to cutting area and the constancy of ploughing force regardless of depth of cut which is over a certain limit each force is separated from measured cutting force and used to establish cutting force model. New rotation matrix to convert the measured cutting force in reference axes into the forces in cutter axes is obtained by considering that tool angles are projected angles from cutter axes to reference axes.. Spindle tilt is also considered far the model. The predicted cutting force estimated from the model is in good agreement with the measured force.

• 한국기계가공학회지
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• 제3권4호
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• pp.20-29
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• 2004

A Tool wear monitoring system is indispensable for better machining productivity with guarantee of machining safety by informing the tool changing time in automated and unmanned CNC machining. Different from monitoring using other signals, the monitoring of spindle current has been used without requiring additional sensors on machine tools. For the reliable tool wear monitoring, current signal only of tool wear should be extracted from other parameters to avoid exhaustive analyses on signals in which all parameters are fused. In this paper, influences of force components of parameters on measured spindle current are investigated and a hybrid approach to cutting force regulation is employed for tool wear signal extraction in the spindle current. Finally, wear levels are verified with experimental results by means of real-time feedrate aspects changed to regulate the force component of tool wear.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.982-986
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• 1997

A method using cutting force signal and neural network for detection tool damage is proposed. Cutting force signal is gained by tool dynamometer and the signal is prepocessed to normalize. Cutting force signal is changed by tool state. When tool damage is occurred, cutting force signal goes up in comparison with that in normal state. However,the signal goes down in case of catastrophic fracture. These features are memorized in neural network through nomalizing couse. A new nomalizing method is introduced in this paper. Fist, cutting forces are sumed up except data smaller than threshold value, which is the cutting force during non-cutting action. After then, the average value is found by dividing by the number of data. With backpropagation training process, the neural network memorizes the feature difference of cutting force signal between with and without tool damage. As a result, the cutting force can be used in monitoring the condition of cutting tool and neural network can be used to classify the cutting force signal with and without tool damage.

• 한국정밀공학회지
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• 제10권2호
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• pp.76-85
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• 1993

Based on the cutting force model, three-dimensional optimal design model was developed and optimal designed tool which is minimized cutting force is developed by computer simulation technique. In this model the objective function which is minimized resultant cutting force was used and the variables are radial rake angle, axial rake angle, lead angle of the tool. The cutting forces using conventional and optimal tools by simulation, are compared and analyzed in time and frequency domains. In time domain the cutting force of optimal tool in feed direction was more reduced and less fluctuated than that of conventional tool. Cutting forces of optimal tool in X-and Z-directions are shown a little increased than those of conventional tool. In frequency domain amplitude of insert frequency components of optimal tool in feed direction was more reduced than that of convent- ional tool. The amplitudes of insert frequency components of optimal tool in X-and Z-direction are a little increased than those of conventional tool. As the reduction of amplitude and fluctuations of the cutting force, Optimal tool is considered that tool life and surface roughness would be improved, and stable cutting would be expected.