• IE interfaces
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• v.12 no.2
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• pp.337-345
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• 1999

This paper presents the optimization of a drilling operation subject to machining constraints such as power, torque, thrust, speed and feed rate. The optimization is meant to minimize the machining time required to produce a hole. For the first time, the effects of a pilot hole are included in the formulation of the machining constraints. The optimization problem is solved by using the geometric programming technique. The dual problem is simplified based on the characteristics of the problem, and the effects of machining constraints on the machining variables are identified.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.100-105
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• 2003

With the recent development of machining technology, high speed machining process is widely used for-the mold and difficult-to -cut-materials machining since it allows achieving high productivity and surface quality. However, during the high speed machining process, high cutting speed and feed rate can cause abrupt tool life decrease due to rapid rising of the cutting tool temperature. Such situation may cause increase of machining cost. Thus, in this study, developed optimization algorithm is applied to determine optimal machining variables for multiple high speed machining. The R-T characteristic curve for machining economics problems with a linear-lorarithmic tool life model is determined by applying sensitivity analysis. finally, a series of high speed machining experiments are performed to determine the desired optimal machining variables, and the results are analyzed.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.185-199
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• 2024

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of process management. Controlling key variables within the process is essential for enhancing the quality of the produced items. In this study, we aim to identify key variables influencing product defects and facilitate quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations) Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM, XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC machining process. Results: By comparing the performances of different models, the selected CatBoost model demonstrated an Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively of negatively impact the dependent variable(good/defective product). We identify variables with relatively low importance, suggesting variables that should be prioritized for management. Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional machine learning techniques. This study holds significance in identifying key variables that should be prioritized for management in CNC machining process. It is expected to contribute to enhancing the production quality of the CNC machining process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.7
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• pp.1-7
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• 2019

A chatter lobe analysis is frequently used to look at the chatter state. Even if there is a lot of research on chatter, chatter lobe characteristics are not well defined. In this study, the chatter lobe behavior according to several variables of vibration mode is verified for further clarity. The dynamic variables of the chatter model are defined and their behaviors on chatter lobe boundary are analyzed in detail. In this sense, the chatter model with 2-DOF (2-DOF) was used to analyze chatter stability characteristics. The discussed results are satisfying and these can be used for the prediction of chatter existence in machining processes of 2-DOF systems in several revolution range. These analyses indicate a better agreement for predicting an appropriate stability lobe over a wide detailed range of critical depths of cut in machining operation. The results allow an excellent prediction of chatter according to various static and dynamic variables in machining states. The behavior of chatter dynamic variables in machining were also discussed in detail. All these results can also be applied to other machining processes by establishing a chatter model in a 2-DOF system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.90-95
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• 2007

Single side final polishing is a very important role to stabilize a wafer finally before the device process on the wafer is executed. In this study, the machining variables, such as pressure, machining time, and the velocity of pad table were adopted. These parameters have the major influence on the characteristics of wafer polishing. We investigated the surface roughness changing these variables to find the optimal polishing condition. Pad, slurry, slurry quantity, and oscillation distance were set to the fixed variables. In order to reduce defects and find a stable machining condition, a hall sensor was used on the polishing process. AE sensor was attached to the polishing machine to verify optimal condition. Applying data analysis of the sensor signal, experiments were performed. We can get better surface roughness from loading the quasi static force and improving wafer-holding method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.28-31
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• 2002

This paper compared the surface roughness with variables before development of centerless grinding using far ferrule machining. In this paper, theoretical surface roughness is obtained from variables such as mesh number, rate of concentration of grinding wheel, wheel rotation of work-piece etc., and optimum condition of machining is selected. For satisfaction the technical side and economical side, centerless grinding using fur ferrule machining should be designed more than #600, 18.8% rate of concentration of grinding wheel, 1440rpm wheel rotation outwork-piece.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.27-36
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• 2005

Some studies have shown that unstable factors are inherent in WEDM process, causing the instability of the discharging pulse to reach about 40∼60％ in normal machining. Transient stability is an important subject in WEDM process since there is a close relationship between stability and machining performance, such as the characteristics of a machined surface, machining speed and problem of instability like wire rupture phenomenon. Among the many machining parameters affecting WEDM machining state, three specific parameters (Vr, Ip, off time ) are major controllable variables that can be applied in transient stability control. And, this research investigates the implementation and analysis of real-time micro control of the discharging stability of WEDM (Wire Electric Discharge Machining) process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.10
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• pp.42-47
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• 2019

Helical machining is an efficient method for machining holes using an endmill. In this study, a surface roughness prediction model was constructed for improving the productivity of hole machining. Experiments were conducted to form holes by the helical machining of AL6061-T4 aluminum sheets and correlation analysis was performed to examine the relationships between the variables based on the measured results. Meanwhile, a regression analysis technique was used to construct and evaluate the prediction model. Through these analyses, the parameter which has the greatest influence on the surface roughness when the hole is formed by the helical machining is the feed, followed by the number of revolutions of the endmill. Moreover, for the axial feed of the endmill, it was concluded that the influence of the surface roughness is small compared to the other two parameters but it is a factor worth considering to improve the accuracy when constructing the predictive model.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.104-118
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• 1998

In this paper, the motion of ballscrew and shape of workpiece are the main objective variables varying with load conditions. To verify feed mechanism in NC lathe, the monitoring system is designed and cutting condition variables are spindle speed depth of cut and feed. During machining, rotation number of ballscrew motion of ballscrew in direction to gravity center and cutting force are measured. After machining, the roughness of workpiece is measured.