• Transactions of Materials Processing
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• v.16 no.1 s.91
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• pp.15-19
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• 2007

This paper presents an integrated machining error compensation method based on captured images of tool deflection shapes in flat end-milling processes. This approach allows us to avoid modeling machining characteristics (cutting forces, tool deflections and machining errors etc.) and accumulating calculation errors induced by several simulations. For this, a high-speed camera captured images of real deformed tool shapes which were cutting under given machining conditions. Using image processes and a machining error model, it is possible to estimate tool deflection in cutting conditions modeled and to compensate for machining errors using an iterative algorithm correcting tool paths. This corrected tool path can effectively reduce machining errors in the flat end-milling process. Experiments are carried out to validate the approaches proposed in this paper. The proposed error compensation method can be effectively implemented in a real machining situation, producing much smaller errors.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.591-596
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• 1999

Due to the development of CNC machining centers and the complexity of machined part geometry, the ball-end milling became the most widely used the cutting process. Generally, the tool runout defined as the eccentricity of a rotating tool set in the holder involved the spindle runout and the problem of tool runout generated to remove the workpiece is a main factor affecting the machining accuracy. In this paper, the relationship of tool runout(zero-to-peak, P-K) and surface shape on the change of cutting conditions is studied and it is proposed the probability of prediction of surface shape from the in-process tool runout measurements with high response displacement sensor in the ball-end milling

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1879-1882
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• 2005

Due to increase of demands on safety and convenience for automotive vehicle, the use of DC motor, such as power window, seat adjusting, pedal adjusting, sunroof, electric shift motor and so on, is increasing rapidly in the whole world. Worm gear is an important part to transmit torque to another gear in gear mechanism of automotive DC motor. But with current forming process, it has some problems in manufacturing and the quality. Also, the characteristics of automotive parts such as price and mass-production limit the quality improvement. Recently several methods are used in order to reduce a worm screw machining time and to maintain precision. In this paper, we introduce whirling tool machining and side milling cutter machining as effective manufacturing method of worm screw and study on the cutting force of side milling cutter and whirling tool in worm screw machining.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.161-167
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• 1996

In five-axis milling, optimal CL-data (cutter location data) should be generated to have advantages over three-axis milling in terms of accuracy and efficiency. This paper presents an algorithm for generating collision-free CL-data for five-axis milling using potential energy method. By virtually charging the cutter and part surfaces with static electricity, global collision as wells as local interference is eliminated. Additionally, machining efficiency is improved by minimizing the curvature difference between the part surface and tool swept surface at a CC-point (cutter contact point) simultaneously.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.1-7
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• 2003

In order to predict vibrations occurred during end-milling processes, the cutting dynamics was modelled by using neural network and combined with structural dynamics by considering dynamic cutting state. Specific cutting force constants of the cutting dynamics model were obtained by averaging cutting forces. Tool diameter, cutting speed, fled, axial and radial depth of cut were considered as machining factors in neural network model of cutting dynamics. Cutting farces by test and by neural network simulation were compared and the vibration displacement during end-milling was simulated.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.19-27
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• 1995

Face milling is required to study cutting process with a view of multipoint cutter. This experimental study mainly deals with the single and multi-insert cutting characteristics using coated tool. Because metal cutting of the single and multi-insert has a large relation to the improvement of productivity, the economic cutting process can be achieved by the analysis of proper metal cutting mechanism. Therefore, machining characteristics of face molling in this paper has been studied by investigating the role of different insert number which is concerned with mean cutting force, the RMS values of AE(acoustic emission) signal, tool life and surface roughness in milling SS 41 and SUS 304. The cutting force and AE signal are monitored to make an analysis of cutting process. The surface roughness of the specimens machined by inserts of different numbers is measured at different speeds, feeds and depth of cut. The width of flank wear is also observed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.26-30
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• 1996

This paper presents the performance of CBN tools in the machining of hardened die-materials, SKD11 and SKD61 steel with HRC 50, by high-speed face milling. Generally, grinding or EDM is being used in machining of hardened materials but the cost is very high. If those can be replaced by cutting, it will be a greatly economical advantage. CBN tool has been recognized as an effective tool in turning, but it has not been in milling. So wear and surface roughness mode of CBN tool for hardened SKD11 and SKD61 steel were investigated by high-speed face milling in this study Also the relation between cutting force and wear mode of CBN tools was investigated.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.5
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• pp.508-513
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• 2015

In this study, the effect of the helix angle and rake angle of a flat end-mill in the milling of titanium alloy was investigated. Tool shape parameters such as helix angle and rake angle affect the cutting force, cutting zone temperature, vibration, and chip flow mechanism, which in turn determine tool life, surface integrity, and dimensional accuracy of the milling process. To investigate the effect of the helix and rake angles, a certain range of parameters was selected, and three-dimensional tool models were generated for finite element analysis (FEA) for each case. The cutting force and pressure on the tool flank face and rake face were investigated by FEA. Further, several tool models were proposed for machining tests. The cutting force characteristics were investigated by the machining tests.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.6
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• pp.43-51
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• 2004

A method for form error prediction in side wall machining with a flat end mill is suggested. Form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. Developed model can predict the surface form error about three hundred times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.