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.

In this study, we carried out interrupted cutting of carbon steel for machine structure(SM20C) with uncoated carbide tool and analyzed anova test and confidence interval to find influential factor to surface roughness, and obtained regression equation. Rhe results are follows: First, we found that affected factor to surface roughness in interrupted cutting was feed rate. Secondly, the cutting speed and depth of cut was small affected to surface roughness. Finally, from multi-regression analysis of interrupted cutting experimental result, obtained regression equation and it’s coefficient determination was 0.814 and it means that regression equation was predictable. Compared with other continuous cutting, if feed rate increase, surface roughness will grow in interrupted cutting.

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

In General, the surface roughness of turning is depends on cutting condition which are cutting speed, depth of cut, and feed, etc. This study was carried out to presume for mutual relation of turning condition to get optimum surface roughness and experimental equation by variance and multi regression analysis As the result, multiple correlation coefficient was calculated 97%, the coefficient of determination (R2) was calculated 95% and the adjusted coefficient was calculated 97% by multiple regression analysis So, the formula was made by this multiple regression analysis is reliable.