• Design & Manufacturing
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• v.17 no.1
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• pp.33-39
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• 2023

The ball end mill is a type of cutting tool that is widely used to process complex mold shapes including aspheric surfaces. Unlike the flat end mill in which the cutting edge is formed on the cylindrical handle, the cutting edge is formed from the cylindrical handle to the hemispherical shape, which is advantageous for processing curved shapes. However, since the cutting speed continuously changes during machining due to the helix angle of the cutting edge or the machining inclination angle, it is difficult to obtain a precise machined surface. Therefore, in this paper, machining was performed while changing the helix angle of the ball end mill and the angle of the machining slope under the same cutting conditions for STD 11 material, which is widely used as a mold material. Through this, the effect of the two variables on the roughness of the machined surface was analyzed. As a result, if the helix angle was 0 degrees, it showed the best surface roughness of Ra. 0.16 ㎛. When the helix angle was 20 degrees, the best surface roughness of Ra. 0.18 ㎛ was occurred.

• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1482-1489
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• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• Design & Manufacturing
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• v.6 no.2
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• pp.84-89
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• 2012

In recent years, technology of press plastic working having made remarkable progress. We can say this because it facilitates mass production and have superior performances in machining speed and equivalency of quality than other processing methods. In characteristics of press plastic working, mold manufacturing according to characteristics of each product should be preceded before processing and it has a great influence on machining speed and quality of products and etc according to manufacturing method. Therefore, mold design technology is a critical technology in press plastic working. There are lots of variables in press plastic working according to worked material, mold materials, conditions of heat treatment, clearance and so on. Abrasion of mold depends on these kind of conditions and sheared surface which is crucial for quality of product also depends on them. In this study, we conduct research on abrasion loss of mold according to 8, 10 and 12% of clearance for thickness of 1.0mm of worked material out of mold design variables of the products whose worked materials are high carbon steel and carbon tool steel by a practical experiment.

• Bulletin of the Korean Space Science Society
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• 2003.10a
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• pp.26-26
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• 2003

During fabrication process for the large space optical surfaces, the traditional bound abrasive grinding with bronze bond cupped diamond wheel tools leaves the machine marks and the subsurface damage to be removed by subsequent loose abrasive lapping. We explored a new grinding technique for efficient quantitative control of precision CNC grinding for space optics materials such as Zerodur. The facility used is a NANOFORM-600 diamond turning machine with a custom grinding module and a range of resin bond diamond tools. The machining parameters such as grit number, tool rotation speed, work-piece rotation speed, depth of cut and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis methods. The effectiveness of the grinding prediction model was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment details, the results and implications are presented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.74-78
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• 2001

In this study, a multi-step optimization technique combined with a simple genetic algorithm is introduce to the structural design optimization of a high speed machining center. In this case, the design problem is to find out the best design variables which minimize the static compliance, the dynamic compliance, and the weight of the machine structure and meet some design constraints simultaneously. Dimensional thicknesses of the thirteen structural members along the static force loop of the machine structure are adopted as design variables. The first optimization step is a static design optimization, in which the static compliance and the weight are minimized under some dimensional and safety constraints. The second step is a dynamic design optimization, where the dynamic compliance and the weight are minimized under the same constraints. After optimization, the weight of the moving body was reduced to 9.1% of the initial design respectively. Both static and dynamic compliances of the optimum design are also in the feasible range even thought they were slightly increased than before.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.23-27
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• 1997

This paper introduces the structural design optimization of a high speed machining center using multi-step optimization combined with G.A.(Genetic Algorithm) and Weighted Method. In this case, the design problem is to find out the best design variables which minimize the static compliance, the dynamic compliance, and the weight of the machine structure simultaneously. Dimensional thicknesses of the thirteen structural members of the machine structure are adopted as design variables. The first step is the cross-section configuration optimization, in which the area moment of inertia of the cross-section for each structural member is maximized while its area is kept constant The second step is a static design optimization, In which the static compliance and the weight of the machine structure are minimized under some dimensional and safety constraints. The third step IS a dynamic design optimization, where the dynamic compliance and the structure weight are minimized under the same constraints. After optunization, static and dynamic compliances were reduced to 62.3% and 95.7% Eorn the initial design, while the weight of the moving bodies are also in the feaslble range.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1307-1313
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• 2011

The mechanical limited slip differential (LSD) in vehicles is being replaced by the electromagnetic LSD because of its fast response and better active control characteristics. The coil housing made of STS 304 is one of the most important parts in the solenoid assembly of the electromagnetic LSD. High geometrical accuracy is a prerequisite for the manufacture of such coil housings, but precision machining is difficult because of the use of STS 304 thin plate and the variance in machining variables. The aim of this study is to optimize the mean and variance of the shape accuracy in the coil housing by finding a robust solution for the machining process conditions. The mean and standard deviation of the jaw contact pressure, cutting speed, and feed rate are considered to be the major parameters for minimizing the geometrical mean and variance. The response surface model based on the second-order Taylor series is combined together to minimize the mean and variance of the shape accuracy of the coil housing.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.3
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• pp.44-54
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• 1991

A Study was made on falnk wear in carbide tools in turning SUS304 steel. When an austenitic stainless steel (SUS304 steel) is cut with the tool, saw-toothed chip are produced. It is found that machining SUS304 steel would make a tool worn fast. For increasing productivity, tool wear has to be predicted and controlled. An amended cutting geometry consisting of a negative rake angle ($-6^{\circ}$ ) and a high clearance angle ($-17^{\circ}$ ) is proposed for decreasing carbide tool wear (flank) in the machining of SUS304 steel. The amended cutting geometry is found to make the flank wear lower than a general cutting geometry (rake angle $6^{\circ}$ , clearance angle $5^{\circ}$). The effects of the three cutting variables (cutting speed, feed, tool radius) on the flank wear analyzed by fiting a simple first-order model containing interaction terms to each flank wear parameter by means of regression analysis and the predicted from first-order regression analysis model equation of flank wear.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.1
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• pp.57-65
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• 2021

With the increased utilization of CAM programs, end-mill processing is most commonly used for machining and metal processing. In particular, hole or shaft machining has high assembly precision, which inevitably leads to high utilization of end mills. However, the analysis of quality characteristics according to the process conditions of end mills is not performed systematically at the site, causing poor quality and productivity. The most influential factor of quality is the runout of the end mill. In this paper, the number of turns of the end mill, number of tool blades, cutting direction, and artificial runout volume were determined to identify the correlation between the epicenter, cylindricality, and surface roughness. Two types of end mills, three levels of runout, three levels of rotational speed, and two cutting directions were considered and 36 rounds of hole processing were conducted. For the analysis of shape characteristics according to the set process variables, the experimental planning method was applied to the measured specimen and the processing characteristics were analyzed according to the runout of the end mill through correlation analysis.