• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.79-89
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• 1998

In determining optimal cutting condition for face milling operation, tool wear is an important factor. For the purpose of establishing the relationship between various machining factors and tool wear, cutting tests have been performed. As a result, hardness and chemical composition of workpiece material, chemical composition and grain size of cutting tool and cutting speed have been selected as machining factors. In addition, relationship between feed rate and workpiece hardness has been observed. Prior to utilizing cutting conditions recommended by ‘Machining Data Handbook(MDH)’ as a knowledge base, an analysis for the validity of the MDH has been provided. Based on this analysis, tool life criteria applied by MDH has been modified. Finally, using MDH recommended data for neural network trainning, the results from the trained neural network for optimal cutting condition for some given workpiece and cutting tool can be used as reference cutting conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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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.

• Journal of the Semiconductor & Display Technology
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• v.15 no.4
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• pp.86-91
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• 2016

End milling is an important and common machining operation because of its versatility and capability to produce various profiles and curved surfaces. This paper presents an experimental study of the cutting force variations in the end milling of SM25C with HSS(high speed steel) and carbide tool. This paper involves a study of the Taguchi design application to optimize cutting force in a end milling operation. The Taguchi design is an efficient and effective experimental method in which a response variable can be optimized, given various control and noise factors, using fewer resources than a factorial design. This study included feed rate, spindle speed and depth of cut as control factors, and the noise factors were different cutting tool in the same specification. An orthogonal array of $L_9(3^3)$ of ANOVA analyses were carried out to identify the significant factors affecting cutting force, and the optimal cutting combination was determined by seeking the best cutting force and signal-to-noise ratio. Finally, confirmation tests verified that the Taguchi design was successful in optimizing end milling parameters for cutting force.