The purpose of using coolant in machining is both to increase a tool life and also to prevent product deformation and thus, stabilize the surface quality by lubricating and cooling the tool and the machining surface. However, a very small amount of cutting mist should be used because chlorine-based extreme pressure additives are used to generate environmental pollutants in the production process and cause occupational diseases of workers. In this study, medical titanium alloy (Ti-6Al-7Nb) was subjected to a processing experiment by selecting factors and levels affecting cutting power in the processing of the Aerosol Dry Lubrication (ADL) method using vegetable oil. The machining shape was a slot to sufficiently reflect the effect of the cutting depth. As for the measurement of cutting force, the trend of cutting characteristics was identified through complete factor analysis. The factors affecting the cutting force of ADL slot processing were identified using the reaction surface analysis method, and the characteristics of the cutting force according to the change in factor level were analyzed. As the cutting speed increased, the cutting force decreased and then increased again. The cutting force continued to increase as the feed speed increased. The increase in the cutting depth increased the cutting force more significantly than the increase in the cutting speed and the feed speed. Through the reaction surface analysis method, the regression equation for predicting cutting force was identified, and the optimal processing conditions were proposed. The cutting force was predicted from the secondary regression equation and compared with the experimental value.

This study investigates on the tuning stainless steel(STS630) to understand for groove cutting characteristics. For this purpose, we observed the cutting force according to feed rate and cutting speed variation and performed the computational analysis due to groove cutting depth. In groove cutting of stainless steel, there were principal force, feed force and radial force by arranging the highest cutting force in order. In case of wall thicknesses of 0.3mm and 0.5mm at groove cutting, principal force increases according to the increase of feed rate but it is not related to cutting speed. We found the unstable region of cutting force that is caused to the friction resistance of cutting tool and elastic deformation of groove wall. In computational analysis, we confirmed that the more feed rate increases, the more strain increases around the tooth root.

In this study, we conducted an interrupted cutting SM20C with lathe and uncoated carbite tool, determined the relationship between Cutting Forces(principal, radial, feed force) by correlation analysis, and predicted the optimum cutting conditions by multiple regression analysis. The result were as follow. : From the correlation analysis, the increase of cutting speed and depth of cut reduces the principal force and radial force. the increase of cutting speed, depth of cut and feed rate will increase the feed force. From multi-regression analysis, we extracted regression equation and the coefficient of determination (R2) was 0.638, 0.692, 0.536 at principal, radial and feed force . It means that the regression equation is not high accuracy. However, it is predictable that the tendency of the forces action the interrupted cutting.

On-line detection system of the abnormal states in a machining process needs to be developed to implement the IMS(Intelligent Manufacturing System). High productivity and efficient quality control can be achieved through the on-condition maintenance for normal tool condition. Generally it is difficult to determine the exact point of time for a tool change because a tool wear grows gradually on the contrary to other abnormal states such as tool fracture, chattering etc. In this article, the shape variation of cutting force signal generated by a insert during face milling was investigated along with a tool wear. The variance, skewness and kurtosis were used as the shape parameters to describe the shape variation and, consequently, utilized as the features to monitor a tool wear. Experimental results showed that the shape parameters could discriminate the tool condition reliably between a fresh tool and a worn tool. As a result, we proposed the method to diagnose a tool wear by combining these parameters with a neural network algorithm.

As the automobile industry has recently developed, the automobile parts processing industry is also increasing. However, as the processing companies don't have their own studies or facilities now, their productivity is low. And they are in difficult situations because they cannot stay economically competitive. This study measured the cutting force, surface roughness, and tool wear of Ductile Cast Iron(FCD500) used in automobile brake modules in automobile parts to try to help harsh realities of automobile parts processing industry. And it drew the result by taking a test to increase productivity of processing companies as interrupted cutting is more used in industrial settings than continuous processing.

In this study, we were turning STD11 and was carried out to presume for mutual relation of turning condition to get optimum cutting force(principal, radial, feed force) and experimental equation by variance, correlation coefficient and multi regression analysis with whisker reinforced ceramic tool. To predict cutting force, analyze principal, radial, feed force with multi-regression analysis. Results are follows: From the analysis of variance, affected factor to cutting force feed rate, depth of cut, cutting speed in order and cutting speed was very small affect to cutting force. From multi-regression analysis, we extracted regression equation and the coefficient of determination(R2) was 0.84, 0.88, 0.7 at principal, radial and tangential force. It means regression equation is significant. From the experimental verification, it was confirmed that princial, radial and tangential force was predictable by regression equation. Through the analysis of the correlation coefficient between each component forces and surface roughness, the principal force was found to have the greatest impact