## 5 Application Examples
### Light Roughing Operation  
A light roughing machining application can be defined as a turning operation with a small turning depth. In fact, these turning operations could be machined with only one pass. The described algorithm allows us to find a tool-insert solution that balances both power consumption and the number of passes. On the other hand, the machining time does not have the same importance as the power consumption as the number of passes is low. Table 9 introduces the working specifications for this light roughing operation. It sets an initial and a final diameter of 50 mm and 40 mm, respectively. This means a total depth of cut of 5 mm. Additionally, the total machining length is set to 100 mm. Figure 8 illustrates this information. The workpiece material is an unalloyed steel of 180 HB. This material has a specific cutting force of 600 N/mm\({}^{2}\). The machine specifications define a small lathe with a power capacity of 10 kW and a maximum spindle speed of 3000 rpm. Equation (27) shows the fitness function for this operation. The power consumption has been weighted to 100 times while the machining time was set to 0.5. This fitness function considers that the impact of power consumption is greater than that of the machining time during the turning process. \[fitness=100\frac{P_{net}}{P}+0.5\frac{T_{life}}{T_{m}}+\frac{Th_{d}}{d_{euclidean}}+\frac{n_{max}}{n_{\text{MC} \_H-\text{MC}\_L}} \tag{27}\]  
Figure 8: Example of a light roughing operation. Figure 9 provides a complete description of the GA optimization. It begins with the definition of the working specifications, which are presented in Table 9. These specifications set the framework for this example. It is important to note that the working specifications are related to the GA model with respect to some stages ahead, such as the random population, feasible solution control, feed rate and cutting velocity models, boundary constraints and fitness evaluation stages. The stability condition establishes some insert grades as possible solutions since this specification was set at \"Good\" in Table 9. Such grade solutions were already shown in Table 8, which indexes the list for good stability. With the stability features already defined, the total chromosome length is defined as 40 for this application. On the other hand, the insert features and working specifications form the chromosome structure. Figure 10 describes the chromosome structure for this application. Given the random nature of the GA model, some infeasible individuals can be part of the population. The feasible solution control prevents infeasible solutions from being part of the GA population. The implementation of this control is introduced in Equation (24). This control evaluates a Euclidean distance by adjusting the \(Th_{d}\) parameter. All individuals out of the range of distance are discarded from the GA population. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Material Specifications**} \\ \hline Initial diameter & \(D_{i}\) & 50 mm \\ Final diameter & \(D_{f}\) & 40 mm \\ Machining length & \(L_{m}\) & 100 mm \\ Hardness & HB & 180 HB \\ Specific cutting force & \(H_{c}\) & 600 N/mm\({}^{2}\) \\ \hline \multicolumn{3}{c}{**Machine Specifications**} \\ \hline Main motor power & \(P_{net}\) & 10 kW \\ Maximum spindle speed & \(n_{max}\) & 3000 rpm \\ \hline \multicolumn{3}{c}{**General Specifications**} \\ \hline Machine operation & CTPT & Roughing \\ Stability & / & Good \\ Tool life & \(T_{life}\) & 15 min \\ Threshold distance & \(Th_{d}\) & 10 \\ Range ap\_rec & ap\_rec & 0.01 mm–5 mm \\ \hline \end{tabular}
\end{table} 
Table 9: Working specifications of the light roughing operation example. Figure 9: Flowchart of the GA implementation. Figure 11 shows the flowchart for obtaining the cutting parameters when given an individual chromosome in the population. It is important to note that some working specifications are used for both the feed rate and cutting velocity neural network models.