## 3 Results and Discussions
### Specific Energy  
Figure 8 shows the relationship between the brittleness indices and specific energy. It is noteworthy that all the specific energy was measured at optimum cutting conditions. For all brittleness indices, the specific energy tended to increase with increases in the index. This means that the cutting resistance and required energy increased as the rock brittleness increased. This finding coincides with the results obtained for the normal force in the earlier section. Among the four indices, B\({}_{3}\) and B\({}_{4}\) had fine correlations with the specific energy, while B\({}_{1}\) and B\({}_{2}\) had a poor correlation with the specific energy.  
The results had good agreement with some previous studies. [PERSON] [26] studied the relationship between rock brittleness (B\({}_{2}\)) and specific energy, finding no significant relationship between specific energy and B\({}_{2}\). [PERSON] [1] also reported that there was no correlation between specific energy values and the brittleness of B\({}_{1}\) and B\({}_{2}\), while he found that B\({}_{4}\) had good correlations with the specific energy of mechanical cutting tools.  
It is a fact that the specific energy generally increases as rock strength increases, because the required cutter force increases as the strength increases. Figure 9 presents the relationship between the UCS and specific energy. The results indicated that the UCS also has a better linear relationship than the brittleness indices. These findings agreed well with a previous study [37], which reported that the specific energy of the optimum condition increased as the uniaxial compressive strength increased.  
Figure 7: Relationship between brittleness indices and mean normal force: (**a**) MNF–B\({}_{1}\); (**b**) MNF–B\({}_{2}\); (**c**) MNF–B\({}_{3}\); (**d**) MNF–B\({}_{4}\).  
Figure 8: Relationship between specific energy and brittleness indices: (**a**) SE–B\({}_{1}\); (**b**) SE–B\({}_{2}\); (**c**) SE–B\({}_{3}\); (**d**) SE–B\({}_{4}\).  
Figure 9: Relationships between UCS and specific energy.  
Table 6 shows the prediction models used for the specific energy obtained from the statistical regression analysis. For B1 and B2, no meaningful prediction model was obtained using multiple non-linear regression analysis. For B3 and B4, a power function-based regression model was obtained. Based on these results, B4 can be considered as an indicator to evaluate cutting efficiency. Further, it was found that the penetration depth, UCS, and BTS had a positive relationship with the specific energy, while the cutter spacing had a negative relationship with the specific energy.  
However, the results indicated that prediction models of specific energy showed lower coefficients of determination (R2) values; this implies that the considered input parameters and amount of data are not enough to accurately predict specific energy, and that the relationships between input and output parameters for regression analysis might be more complicated than that of this analysis (based on multiplication of power function). As reported by the previous studies [36; 37; 41], the specific energy changed with the combination of penetration depth and cut spacing. This specific energy curves can usually be represented by a convex function with a minimum point, and this minimum point indicates the optimum cutting condition. [PERSON] et al. [37] also reported that the characteristics of specific energy curves depends on the rock type (or strength), values of penetration depth, and cut spacing. Consequently, it means that the database should be able to efficiently cover the range of each variable for the regression analysis; an extended database and further analysis will be required.