## 3 Results and Discussions
### Optimization Using TLBO Algorithm  
The TLBO algorithm was employed to obtain the desired values of responses by evaluating the important levels of design variables. TLBO was executed by considering BW as minimization, and BH as maximization. The levels of process variables used during the implementation of algorithms include WFS, \(4\leq\text{WFS}\geq 6\); travel speed, \(425\leq\text{S}\geq 485\); and voltage, \(19\leq\text{V}\geq 21\). The TLBO algorithm was implemented for single-response optimiza  
Figure 6: Residual plot for BW.  
Figure 7: Residual plot for BH.  
To obtain the desired optimal levels for BW and BW simultaneously, multi-objective teaching-learning-based optimization (MOTLBO) was employed. MOTLBO is a multi-objective form of the TLBO algorithm. MOTLBO was proven to be vastly capable to deal with two or more objectives simultaneously and has shown the ability to produce non-dominant solutions for conflict situations. In the current study, MOTLBO has produced 48 optimal non-dominant unique solutions for BW and BH. Table 7 presents these unique and independent values of responses, along with their respective process variables. The Pareto curve of these optimal points was generated, as shown in Figure 9. In the present study, as discussed earlier, BW was considered as a minimization criterion while BH was a maximization. The contradictory nature between BW and BH can be observed from the Pareto chart. For minimum BW, the value of BH was also the lowest, and vice-versa. Therefore, by considering the need for the required values of BW and BH, the user can select the corresponding levels of process parameters from Table 7 and Figure 9. In addition, Pareto points can be generated if required by employing the MOTLBO algorithm. Validation experiments were performed by randomly selecting five Pareto points to validate the results of the TLBO algorithm. The discrepancy between the anticipated and measured values was observed to be negligible, with a deviation of less than 5% for all the five trials. This demonstrates the success of the established model and TLBO algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sr. No.**} & \multirow{2}{*}{
\begin{tabular}{c} **WFS** \\ **(m/min)** \\ \end{tabular} } & **Travel Speed** & **Voltage** & **BW** & **BH** \\  & & **(mm/min)** & **(V)** & **(mm)** & **(mm)** \\ \hline
1 & 6.2 & 425 & 18 & 7.65 & 8.37 \\ \hline
2 & 5 & 485 & 18 & 4.53 & 4.85 \\ \hline
3 & 6.2 & 471 & 18 & 6.78 & 7.74 \\ \hline
4 & 6.1 & 474 & 18 & 6.53 & 7.47 \\ \hline
5 & 6 & 485 & 18 & 6.27 & 7.10 \\ \hline
6 & 5.5 & 478 & 18 & 5.28 & 6.07 \\ \hline
7 & 5.5 & 485 & 18 & 5.27 & 5.97 \\ \hline
8 & 6.2 & 463 & 18 & 6.86 & 7.85 \\ \hline \end{tabular}
\end{table}
Table 7: Non-dominated unique solutions obtained from TLBO.  
Figure 8: Optical micrograph for (**a**) minimization of BW, (**b**) maximization of BH.  
as 7 mm, and 6.07 mm, respectively, at WAAM process parameters of WFS of 5.9 m/min, travel speed of 476 mm/min, and voltage of 18 V. Figure 10 shows the multi-layer structure obtained at these optimized parameters. It shows the structure is free from any disbonding and a perfect fusion between the layers can be observed. It was observed that on the extreme sides of the deposition, there was an additional lump of metal. This is due to the usage of metal-cored wires for GMAW. However, the start and stop are always scrapped in the post processing. This demonstrates the suitability of the TLBO technique for accurately suggesting the process parameters for WAAM.  
Figure 10: Multi-layer structure obtained at optimized parameters.  
Figure 9: Pareto curve of BH vs. BW.