• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.2
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• pp.21-26
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• 2005

From on the machining center cutting work of 5534, the characteristics such as spindle speed and feed speed fir the third point height, average spacing of roughness peaks, bearing ratio, center line average, ten point height. experiments is roughness for sampling length determine to measuring length of cutting feed speed 200, 400, 600, 800mm/min and spindle speed 800, 1000, 1200, 1400rpm. Third point height is spindle speed with most suitable cutting condition 1000rpm. Third point height is feed speed with most suitable cutting condition 400mm/min. Average spacing of roughness peaks are spindle speed with most suitable cutting condition feed speed increased to average spacing of roughness Peaks are increased. Spindle speed increased to average spacing of roughness peaks are decreased. Bearing ratio is spindle speed with feed speed increased to bearing ratio decreased. Center line average is spindle speed with most suitable cutting condition at 1200rpm feed speed with most suitable cutting condition at 200mm/min to cutting foe roughness suddenly decreased. Ten point height is spindle speed with most suitable cutting condition 1200rpm at ten point height cutting face roughness to decreased and feed speed with most suitable cutting condition 800mm/min at ten point height cutting face roughness to decreased.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.111-117
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• 2002

In most machining companies, operators decide the cutting condition, a pair of spindle speed (5) and table federate (F) by experience and subjective judgment. As cutting conditions are determined by operators' experience and ability, inconsistent cutting conditions are given in same operating conditions. The objective of this study is to develop the cutting condition decision system which utilizes shop data and predicts tool life by neural network and eventually leads to the optimal cutting condition. The production time per piece is considered for an optimization object. We will discuss the process of an optimal cutting condition decision by neural network. By this process, a series of shop data is stored. And neural network is constructed for prediction of tool life and the optimal cutting condition is recommended from a cutting condition decision system using the stored shop data. The results show that the developed system is rational in searching the optimal cutting conditions on job operations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1784-1788
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• 2003

For the improvement of geometrical accuracy in end milling, cutting method and cutting condition selection are investigated in this paper. As machining processes are composed of several steps such as roughing, semi-finishing. and finishing, cutting forces and tool deflection are calculated considering surface shape generated by the previous cutting. The effects of tool teeth numbers, tool geometry, and cutting conditions on the form error are analyzed. Using the from error prediction method from tool deflection, cutting condition for geometrical accuracy improvement is discussed. The characteristics and the difference of generated surface shape in up and down milling are dealt with and over-cut free condition in up milling is presented. The form error reduction method by alternating up and down milling is also suggested. The effectiveness of the presented method is examined from a set of cutting tests under various cutting conditions. This research contributes to cutting process optimization for the geometrical accuracy improvement in die and mold manufacture.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.965-969
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• 1997

Due to the high feed rate,high speed machining (HSM) provide a great potential of rationalization for the machining Dies and Moulds. But determination of cutting condition is very difficult, because cutting mechanism of high speed machining is very complicated,especially using ball end-mill. This paoer gives a report on selection of the optimal cutting condition to improve the machining efficiency, And optimal machining condition is determined through the cutting force, FFT analysis of cutting force and surface roughness according to the cutting condition. Based on this experiment result,wear process and machining characteristics are evaluated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.169-172
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• 1995

In optmizing cutting condition for face milling operation, tool wear is an important maching factor. For the purpose of establishing the relationship between various maching factor and tool wear, cutting tests have been performed. As a result, hardness and chemical composition of workpiece material, chemical compositition and grain size of cutting tool and cutting speed have been selected as machining factor. In addition, relationship between feed rate and workpiece hardness has been observed. Prior to utilizing cutting condition recommended by 'Machining Data Hardbook(MDH)' as a Knowledge base, an analysis for the validity has been provided. Based on this analysis, tool life criteria applied by MDH has been modifiied. Finaly, using MDH recommended data for neural network trainning, we can compensate the result form the trained neural network for optimizing cutting condition for some given workpice and cutting tool.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.31-40
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• 2003

In this paper, optimal cutting condition to minimize the form error in side wall machining with a flat end mill is studied. Cutting forces and tool deflection are calculated considering surface shape generated by the previous cutting such as roughing. Using the form error prediction method from tool deflection, optimal cutting condition considering form accuracy is investigated. Also, the effects of tool teeth number, tool geometry and cutting conditions on form error are analyzed. The characteristics and the difference of generated surface shape in up and down milling are discussed and over-cut free condition in up milling is presented. Form error reduction method through successive up and down milling is also suggested. The effectiveness and usefulness of the presented method are verified from a series of cutting experiments under various cutting conditions. It is confirmed that form error prediction from tool deflection in side wall machining can be used in optimal cutting condition selection and real time surface error simulation for CAD/CAM systems. This study also contributes to cutting process optimization for the improvement of form accuracy especially in precision die and mold manufacturing.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.4
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• pp.419-424
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• 2004

In order to increase the efficiency of the sawdust machine, the helical type drums with an oblique cutting condition (3D) were developed. The preexisting drums have an orthogonal cutting condition (2D) considering of a saw cutter and a wood cutting pattern. Unfortunately, the disadvantages of this type drum are the big cutting resistance, the high cost of a cutting power and the extreme occurrence of a vibration. To improve this shortcoming, the helical type drum with an oblique cutting condition was developed. Therefore, this type drum decreased the vibration and the cutting resistance of the sawdust machine, and also improved the productivity of 30% or more and the porosity of the sawdust.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.88-92
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• 2004

We developed a helical type drum with an oblique cutting condition for sawdust machine. The existing drum has an orthogonal cutting(2D) condition considering of saw cutter and wood cutting pattern. Additional disadvantage of this type include big cutting resistance, high cost of cutting power and extreme occurrence of vibrations. To improve this shortcoming, we developed a helical type drum with an oblique cutting(3D) condition, therefore, this type drum decreased the vibration and cutting resistance of sawdust machine, and also improved productivity and sawdust porosity.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.62-70
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• 1996

We experimented on cutting characteristics-cutting force, behavior of cutting temperature, surface roughness. chip thickness under low temperature, which generated by liquid nitrogen(77K). The work-pieces were freezed to-195 .deg. C and liquid nitrogen was also sprinkled on cutting area in order to decrease an experimental error of machining in low temperature. The workpiece was became to -195 .deg. C in5 minutes. In cooled condition surface roughness of workpiece was better than normal condition. In addition, we investigated the possibility that surface roughness of workpiece and cutting force can be predicted analyzing cutting conditions by the trained neural network.