In General, the cutting forces and surface roughness at turning depends on the cutting conditions which are consisted with the cutting speed, depth of cut, and feed rate, etc
In this study, turnting on cold work tool steel(STD11) and hot work tool steel(STD61) using general purpose lathe was carried out. The tools used were whisker reinforced ceramic tools and the radius of tools were fixed on 0.8mm. The cutting conditions were consisted with the three independent variables the cutting speed, depth of cut, and feed rate. According to the cutting conditions, the variations of principal force, radial force, feed force and surface roughness were recorded. The recorded results were analysed by the main effect analysis after the analysis of variance. For the optimal regression analysis, Variables were selected. Through regression analysis,
The results of the regression analysis, was able to predict the cutting force and surface roughness under various cutting conditions with different materials. As a result of the regression analysis, to predict the cutting force and surface roughness under various cutting conditions with different materials has been possible, and thus is expected to control the cutting conditions to obtain the desired results.

The study led the following conclusions.

(1) In case of turning of two different test pieces using whisker reinforced ceramic tools, the result of the analysis of variance about the cutting forces of each piece showed the most influential factor to principal force and radial force. It was feed rate, the second biggest factor was the depth of cut, and the cutting speed followed by. When it comes to the feed force, the cutting speed occupied very small portion of factors, depth of cut was the most influential factor. An examination of correlation coefficient between independent variables and dependent variables was the same result as the analysis of variance.

(2) The analysis of variance about each factors presented feed rate to the most influential factor to the surface roughness. Because of the value of the analysis of variance of feed rate is big enough to offset the rest of factors, the surface roughness is possible to predict with only feed rate. Moreover, the correlation coefficient was analyzed between the each forces as a dependent variable and the surface roughness as another dependent variable. The principal force gave most influence to surface roughness in the case of STD11, and in the case of STD61, the radial force and the principal force is similar.

(3) This study found some equation that can predict each cutting forces and surface roughness according to the cutting speed, depth of cut, and feed rate.
At the cutting of STD11 with whisker reinforced ceramic tools, the value of (adj) of the principal force was 0.82 by linear multi-regression analysis and 0.84 power equation, the value of (adj) of the radial force was each 0.717 and 0.762, and the value of (adj) of the feed force was each 0.714 and 0.7. The value of (adj) of the surface roughness was 0.92 by linear multi-regression analysis and 0.963 by power equation.

(4) At the cutting of STD61 with whisker reinforced ceramic tools, the value of (adj) of the principal force was 0.894 by linear multi-regression analysis, and 0.873 by power equation. The value of (adj) of the radial force was each 0.889 and 0.863, and the value of (adj) of the feed force was each 0.855 and 0.817. The value of (adj) of the surface roughness was 0.949 by linear multi-regression analysis, and 0.959 by power equation.

In this study, the regression models were searched, which can be used to turning of high hard steel with whisker reinforced ceramic tools. For this, all workable regression analysis to all variables were done, and suitable independent variables were founded.
Through the regression analysis, the mathematical models were also found. This models are calculated from given cutting conditions, and can measure the principal force, radial force, feed force, and the surface roughness. Through the prediction of the cutting force and surface roughness, the predicted values have been checked up moving along the slope difference between linear multi-regression equation and power equation.

In this study, the eight equations were produced. If choose one of them to fit the situation or materials, and setting the cutting speed, depth of cut, and feed rate, then can calculate the rough of the cutting conditions and surface roughness. Until now the cutting of general purpose lathe have been largely based on the experience. This study will build a system of the cutting. It makes to determine easily the cutting conditions, and to expect to save time cost.

To develop this study, it will be possible to apply high-order polynomial regression analysis to this study, and will be possible to take additional independent variables such as a coolant, or tool radius. Also it will be possible to apply this study on high speed processing, precision processing, and CNC machine.