• 대한산업공학회지
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• 제26권2호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.519-523
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• 1996

The polishing tool unit is developed which can be attached on the machining center and automatic polishing system for die and mold is also established. So, experiments are carried out under the several polishing conditions. The polishing properties of automatic polishing operation such as surface roughness, stock removal and maximum profile valley depth are obtained and analyzed quantitatively for each polishing tool. The purpose of this study is to construct an expert system for optimal condition of die and mold automatic polishing using the knowledge base that is obtained in the polishing experiments and, to verify the feasibility of the expert system through the experiments. Using this expert system, unskilled operators will be able to obtain the knowledge and experience of an expert.

• 한국생산제조학회지
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• 제20권3호
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• pp.232-238
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• 2011

In the present study, Taguchi method is used to determine the rough region first, followed by RSM technique to determine the exact optimum value during milling on a machining center. A region reducing algorithm is applied to narrow down the region of the Taguchi method for RSM. The result from the Taguchi method is fed to train the artificial neural network (ANN), whose optimum value is used to drive the region reducing algorithm. The proposed algorithm is tested under different cutting condition and results show that the introduced algorithm works well during milling process. It is also shown that theoretically obtained optimal cutting condition is very close to experimentally obtained result.

• Design & Manufacturing
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• 제17권4호
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• pp.1-7
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• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.1038-1043
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• 1997

To adapt the neural network proess for the purpose of determination of optimal utting onditions (optimal cutting speed and feed rate), some selection strategies for the machining factors are necessary, which is considered planning cutting process. In this case, factors that have both nonlinearity and strong relationship must be selected. Although tool grade and chemical properties of workpiece material have strong effect to cutting speed, it's not easy to find a analytic relation between them. In this paper, a mathematical method for determining the optimal amount of cutting (depth of cut, feed rate) is presented by tool goemetry and heat generation during cutting process. And various tool grade and workpiece material groups ase classified based on its chemical properties. Thier chemical composition and hardness are used as input pattern for neural network learnig. The result of learning shows the relationship between tool grade and workpiece material and it is proved that it can be used as a sub-system for automatic process planning system.

• Design & Manufacturing
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• 제11권1호
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• pp.50-54
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• 2017

In this study, researches were conducted as follows. First, as the basic experiment, the cutting speed, feedrate, and the depth of cut were set as the process parameters, and by setting the surface roughness as the factor of measurement for each of the combinations, and the analysis about cutting tendency of the material was conducted by proceeding the turning process of Co-Cr-Mo alloy. Second, by setting the feature of the surface roughness according to the 'turning processing condition' that was confirmed in the previous experiment, and by applying the Taguchi Method, the conditions that influence the features of the surface roughness according to the 'turning processing condition' of Co-Cr-Mo was analyzed, and also by measuring the surface roughness according to each of the 'cutting conditions', the optimal processing condition was generated. As the result of analysis, it was possible to understand that the factor that mostly affects the surface roughness was the cutting speed, followed by the dept of cutting and transfer speed, and as for the optimal processing condition, it was possible to find that the cutting speed was 5,000rpm, and the depth of cut was 0.1mm, and the feedrate was 0.003mm/rev, and the value of the surface roughness at this point is $0.197{\mu}m$.

• 한국기계가공학회지
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• 제3권2호
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• pp.3-9
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• 2004

The main objectives of this paper are to determine optimum cutting conditions using experimental design method to manufacture pedicle screws. Generally, titanium alloys are known as difficult-to cut materials. In the machining of titanium alloy, high cutting temperature and strong chemical affinity between the tool and the work material are generated because of Its low thermal conductivity and chemical reactivity. Such phenomenon cause increase of tool wear and deterioration of surface quality. Thus, in this paper, required experimental investigations are performed to evaluate the machinability of titanium materials With tungsten carbide tools Required simulation and experiments are performed, and the results are investigated.

• 한국생산제조학회지
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• 제20권4호
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• pp.495-501
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• 2011

The purpose of this study is to improve the roundness of CNC turning so that helps the operator to choose the right turning conditions to produce a product with the given parameters. This paper focuses on determining the optimal levels of machining factors for circular shaft with CNC turning. For this purpose, the optimization of factors is performed based on experimental design method. A design and analysis of experiments are conducted to study the effects of these factors on the roundness by using the SIN ratio, analysis of ANOVA, and F-test. Factors, namely, fixed pressure, wall thickness, depth of cut, and feed rate are optimized with consideration of the roundness. The boring tool used in this study is a tungsten carbide coated. The material of workpiece is Al6061 and the machining method is dry cutting.

• 한국생산제조학회지
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• 제26권4호
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• pp.402-409
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• 2017

Selection of optimal cutting conditions is important for improving productivity and implementing efficient process control in metal machining. In this study, improvement of cutting conditions in machining using end-mills is studied by using deep-layered neural networks, which comprise an input layer, output layer, and two hidden layers. System networks are designed with inputs as cutting conditions, and they output the cutting force. A pseudo-inverse network is designed that has the adjustable cutting condition as output and cutting force and other cutting conditions as input. The combination of the system network and pseudo-inverse network enables selection or improvement of cutting conditions that results in the expected cutting force.

• 한국정밀공학회지
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• 제19권8호
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• pp.77-83
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• 2002

This research aimed at from the establishment of theory on micro electrochemical machining mechanism to the implementation of a practical fabrication system of micro parts. In detail, the mechanism of micro-ECM was investigated with potentiodynamic method and the optimal condition for micro-ECM was selected by voltage-current-time curve with potentiostatic method. From the experimental result, the micro part which has extremely fine surface could be fabricated by use of micro-ECM with point electrode method.