• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.4
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• pp.498-503
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• 2010

The main purpose of this study strongly concerned micro machining error estimation by using FEM analysis of tool deflection shapes in micro flat end-milling process. For the precision micro flat end-milling process, analysis of micro cutting errors is mandatory. In general, tool deflection is a major factor which causes cutting error and limits realization of the high-precision cutting process. Especially, in micro end-milling process, micro tool deflection generates very serious problems in contrast to macro tool deflection. Methods which deal with compensation of cutting error by tool deflection in macro end-milling process have been studied plentifully but, few researches transact with micro scaled cutting tool deflection in micro cutting process. Therefore, the trend of micro tool deflection was estimated by using FEM analysis in this paper. Cutting forces were acquired by micro dynamometer and these were utilized in FEM analysis. In order to verify FEM analysis results, micro machining processes were carried out and real machined profiles were compared with FEM results. Finally through the proposed approach well suited FEM results were obtained.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.105-110
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• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.76-83
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• 2006

Machining accuracy is closely related with tool deflection induced by cutting forces. In this research, cutting forces and tool deflection in end milling are expressed as a closed form of tool rotational angle and cutting conditions. The discrete cutting fores caused by periodic tool entry and exit are represented as a continuous function using the Fourier series expansion. Tool deflection is predicted by direct integration of the distributed loads on cutting edges. Cutting conditions, tool geometry, run-outs and the stiffness of tool clamping part are considered together far cutting forces and tool deflection estimation. Compared with numerical methods, the presented method has advantages in prediction time reduction and the effects of feeding and run-outs on cutting forces and tool deflection can be analyzed quantitatively. This research can be effectively used in real time machining error estimation and cutting condition selection for error minimization since the form accuracy is easily predicted from tool deflection curve.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.12
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• pp.1185-1191
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• 2011

The kinematic characteristics of cutting device significantly affects cutting performance in 2-dimensional elliptical vibration cutting(EVC) where the cutting tool cuts workpiece, traversing a micro-scale elliptical trajectory in a trochoidal motion. In this study, kinematical characteristics of EVC device constructed with two parallel stacked piezoelectric actuators were analytically modeled and compared with the experimental results. The EVC device was subjected to step and low-frequency(0.1 Hz) sinusoidal inputs to reveal only its kinematical displacement characteristics. Hysteresis in the motion of the device was observed in the thrust direction and distinctive skew of the major axis of the elliptical trajectory of the cutting tool was also noticed. Discrepancy in the voltage-to-displacement characteristics of the piezoelectric actuators was found to largely contribute to the skew of the major axis of the elliptical trajectory of the cutting tool. Analytical kinematical model predicted the cutting direction displacement within 10 % error in magnitude with no phase error, but in estimating the thrust direction displacement, it showed a $27^{\circ}$ of phase-lag compared with the measured displacement with no magnitude error.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.2
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• pp.128-134
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• 2008

This paper presents a machining error compensation methodology due to deflection of micro cutting tools in side cutting processes. Generally in order to compensate for tool deflection errors it is necessary to carry out a series of simulations, cutting force prediction, tool deflection estimation and compensation method. These can induce numerous calculations and expensive costs. This study proposes an improved approach which can compensate for machining errors without simulation processes concerning prediction of cutting force and tool deflection. Based on SEM images of test cutting specimens, polynomial relationships between machining errors and corrected tool positions were induced. Taking into account changes of cutting conditions caused by tool position variation, an iterative algorithm was applied in order to determine corrected tool position. Experimental works were carried out to validate the proposed approach. Comparing machining errors of nominal cutting with those of compensated cutting, overall machining errors could be remarkably reduced.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.781-785
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• 2005

Cutting forces and tool deflection in end milling are represented as the closed form of tool rotational angle and cutting conditions. The discrete cutting forces caused by tool entry and exit are continued using the Fourier series expansion. Tool deflection is predicted by direct integration of the distributed loads on cutting edges. Cutting conditions, tool geometry, run-outs and the stiffness of tool clamping pan are considered for cutting forces and tool deflection estimation. Compared to numerical methods, the presented method has advantages in short prediction time and the effects of feeding and run-outs on cutting forces and tool deflection can be analyzed quantitatively. This research can be effectively used in real time machining error estimation and cutting condition selection for error minimization since the ferm accuracy is easily predicted by tool deflect ion curve.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.721-724
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• 2000

This paper presents a prediction of tool deflection and resulting machining error fur sculptured surface productions in the ball-end milling process. Due to the different materials and the dimensions of the tool holder and cutter, a cantilever hem model with three uniform sections is proposed fur the tool deflection model. The ability of this model has been verified by a machining experiment. In this study, cutting force and machining error are investigated. This paper provides the prediction of machining error for sculptured surface to improve machining quality for industrial application.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.2
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• pp.121-127
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• 2008

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. Despite the importance of micro end-milling, many related researches have given grand efforts 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. To solve this problem, machining error prediction method was proposed through a series of test micro cutting and analysis of their SEM images. An iterative algorithm was applied in order to obtain corrected tool path which allows reducing machining errors in spite of tool deflection. Experiments are carried out to validate the proposed approaches. In result, remarkable error reduction could be obtained.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.62-66
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• 2004

A study for investigating the effects of the cutting conditions(feed rate, radial depth of cut, cutting speed) and the tool diameter on the circumferential geometry of the cyl indrically end-mi1led workpiece is described. In this work, the circumferential geometry is characterized by the roundness error. Experimental results show that the circumferential geometry is directly affected by the material removal per tooth,which is defined as a function of the cutting speed, the feed rate and the radial depth of cut. And, the radial depth of cut is revealed to be the most critical condition among them. It is also found that the roundness error decreases when the tool diameter is larger under the same cutting conditions.