• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.2
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• pp.68-74
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• 2013

In this paper, an average surface roughness, $R_a$, was measured by optical measurement and its mathematical model according to spindle speed and feedrate was obtained by least square method. Also, its result is compared and investigated with real measured average surface roughness. The optical measurement of surface roughness is performed by CLSM(confocal laser scanning microscope) and the captured HEI(height encoded image) data is used as an original data for the generation of average surface roughness and its mathematical plane or contour surface of surface roughness. Using this polynomial model with two independent variables, the behavior of an average surface roughness is investigated and analyzed with an experimental modeling of least square algorithm. And it can be used for the prediction of $R_a$ in different condition of machining.

• Transactions of the Korean Society of Mechanical Engineers
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• v.9 no.5
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• pp.590-597
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• 1985

A recursive geometric adaptive control method to compensate for machining straightness error in the finished surface due to tool deflection and guideway error generated by end milling process is developed. The relationship between the tool deflection and the feedrate is modeled by a modified Taylor's tool life equation. Without a priori knowledge on the variations off cutting parameters, time varying parameters are then estimated by an exponentially windowed recursive least squares method with only post-process measurements of the straightness error. The location error is controlled by shifting the milling bed in the direction perpendicular to the finished surface and adding a certain amount of feedrate with respect to the tool deflection model before cutting. The waviness error is compensated by adjusting the feedrate during machining. Experimental results show that location error is controlled within a range of fixturing error of the bed on the guideway and that about 60% reduction in the waviness error can be achieved within a few steps of parameter adaption under wide operating ranges of cutting conditions even if the parameters do not converge to fixed values.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.111-117
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• 2016

The cutting performance of a developed small-angle spindle tool was investigated with Al6061 using a TiAlN coated high-speed steel end mill. Up-cut and down-cut processes in a milling machine were carried out at the range of 1000-4000 rpm for spindle speed and 50-300 mm/min for feed rate. As a result, the highest cutting force in the Fx direction was obtained from the up-cut process when the spindle speed was 1000 rpm and the feed rate was 100 mm/min. In the Fy direction, the highest cutting force appeared in the up-cut process at a feed rate of 250 mm/min at the same spindle speed. Conversely, the lowest cutting force came out in the up-cut process at a spindle speed of 4000 rpm and a feed rate of 50 mm/min. As for surface finish, the finest surface roughness was obtained as Ra 0.7642 um at a spindle speed of 4000 rpm and a feed rate of 50 mm/min. Consequently, given the cutting performance of the developed small-angle spindle tool, we conclude that its use in industrial practice is feasible.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Structural Engineering and Mechanics
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• v.76 no.2
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• pp.175-191
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• 2020

In this study, the effect of Nano silica (SiO2) on the buckling strength of the glass fiber reinforced laminates containing the machining process causes holes were investigated. The tests have been applied on two status milled and non-milled. To promote the mechanical behavior of the fiber-reinforced glass epoxy-based composites, Nano sio2 was added to the matrix to improve and gradation. Nano sio2 is chosen because of flexibility and high mechanical features; the effect of Nanoparticles on surface serenity has been studied. Thus the effect of Nanoparticles on crack growth and machining process and delamination caused by machining has been studied. We can also imply that many machining factors are essential: feed rate, thrust force, and spindle speed. Also, feed rate and spindle speed were studied in constant values, that the thrust forces were studied as the main factor caused residual stress. Moreover, entrance forces were measured by local calibrated load cells on machining devices. The results showed that the buckling load of milled laminates had been increased by about 50% with adding 2 wt% of silica in comparison with the neat damaged laminates while adding more contents caused adverse effects. Also, with a comparison of two milling tools, the cylindrical radius-end tool had less destructive effects on specimens.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.3
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• pp.26-33
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• 2003

The term 'High Speed Machining' has been used for many years to describe end milling with small diameter tools at high rotational speeds, typically 10,000-100,000rpm. The process was applied in the aerospace industry for the machining of light alloys, notably aluminum. In recent year, however, the mold and die industry has begun to use the technology for the production of components, including those manufactured from hardened tool steels. With increasing cutting speed used in modern machining operation, the thermal aspects of cutting become more and mole Important. It not only directly influences in rate of tool weal, but also affects machining precision recognized as thermal expansion and the roughness of the surface finish. Hence, one needs to accurately evaluate the rate of cutting heat generation and temperature distributions on the machining surface. To overcome the heat generation, we used to cutting fluid. Cutting fluid plays a roles in metal cutting process. Mechanically coupled effectiveness of cutting fluids affect to friction coefficient at tool-workpiece interface and cutting temperature and chip control, surface finish, tool wear and form accuracy. Through this study, we examined the behavior of heat generation in high-speed machining and the cooling performance of various cooling methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.1044-1048
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• 1997

Wire-cut EDM is used in Die manufacturing as the part of non-traditional cutting process, But,the determination of it's cutting condition with high efficiency and precision is difficult due to the influence of cutting environment and cutting mechanism. In this study, we examine the cutting performance of the SKD11 and Brass in wire-cut EDM and make the neural network which have the configuration of 5-12-2 and back-propagation learning rule. Through the neural network, we can appraise the cutting performance before working and determine the optimal cutting condition. By introducing this method to the W-cut EDM, we can enhance the cutting efficiency.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.165-170
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• 2000

In order to perform a successful material cutting process, the operators are to select the suitable machining tools and cutting conditions for the cutting environment. Up to now, this has been a complicated procedure done by the data in the tool manufacturers' paper catalog and the operator's experiencial knowledge, so called heuristics. This research is motivated by the fact that using computer techniques in processing vast amount of data and information, the operator can determine the tool and cutting condition easily. In the developed program, the selection of milling cutter, insert, and components are combined to provide optimal cutting speed, depth of cut, feed rate, rpm, and power. This program also provides the selection routine for end mill, drilling, turning, and grinding where the suitable tools are selected by workpiece, holder type, cut type, and insert shape.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.121-126
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• 2000

This research proposes a new advanced control method and demonstrates its realization in part. By incorporating shape machining and cutting force control at a time, this integrated scheme makes it possible to machine a desired shape and avoid the trouble of programming feedrate and spindle speed before machining and also reduce the shape error. The main idea proposed to achieve those goals consists in giving commanded path and desired cutting force at the same time. which makes it possible for position and force controller to distribute the corresponding velocity of individual axes and main spindle by an appropriate interpolation. That indicates we can replace the built-in interpolator of commercial machine tools by the developed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.99-107
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• 1997

An algorithm is developed to find optimal machining parameters for multiple machining environments. The cutting rate-tool life (R-T) characteristic curve presents the general loci of optima and is useful for the flexible machining operations planning. The R-T characteristic curve for the machining economics prob- lems with linear-logarithmic tool lofe model may be determined by applying sensitivity analysis to log-dual problems. Three cases of the change of machining environments are considered. An end milling example is constructed to illustrate the algorithm.