## 4 Discussion
The optimised process parameters resulted in a ratio between the laser power and laser spot size of \(P/d_{s}=\)720 W mm\({}^{-1}\) and a specific energy of \(E=\)61.51 J mm\({}^{-2}\); other research [29] deposited 18 Ni300 tensile specimens using an orthogonal DOE between the laser power, scanning speed and feeding rate; the specimen produced with a specific energy of 67.7 J mm\({}^{-2}\) achieved the highest ultimate tensile strength. Additional research [28] optimised process parameters for 18 Ni300 processing, obtaining specific energy of 41.95 J mm\({}^{-2}\) in the optimised parameters for dilution proportion. A comparison between process variables the present work and the aforementioned research is visualised in Figure 13.  
Regarding the developed algorithm and its output results, the width values proved to be the computed variable displaying the smallest error percentage, with the average error being 2.71%. This constitutes a satisfying error for the computed width, with no particular  
Figure 13: Comparison between the specific energy \(E\) and ratio between scanning speed and feeding rate \(v_{s}/f_{r}\) between the present work and other research: [PERSON] et al. [29], [PERSON] et al. [28] and [PERSON] et al. [39]. The parameters shown by [28] were optimised for dilution \(\geq\) 10%, and the work by [PERSON] et al. [29] where the parameters resulted in a specimen with the largest tensile strength. outlier or track whose error is significant or would otherwise highlight a shortcoming in the algorithm. Regarding the height, track 2 shows a 8.81% error, attributable to a small chip developed during the cutting procedure of the sample that remained attached to the deposited part; this increase in height was not considered when manually measuring the height but was taken into account by the algorithm. The penetration limit displayed two very evident outliers--track 32 and track 35--with both cases underestimating the values. In the former, the substrate line is significantly asymmetric--shown in Figure 12c, with the right and left sides of the track being misaligned by circa 50 px; as the computed substrate line sits near the substrate side, which is located nearer to the penetration limit, its value is underestimated when compared to the manually determined one. This measurement could be improved by considering the differential between the substrate height to the left and to the right hand sides of the bead. Alternatively, track 35--Figure 12d--miscalculated the penetration limit; the dilution zone of this track is asymmetric and presents a bulge to the left side of the dilution zone, which the algorithm failed to include, thus underestimating the penetration limit and the area below the substrate. This could be avoided by additional criteria in detecting the penetration limit in addition to the maximum of the derivative of the filtered sum of pixel values by row, similarly to the additional criteria used when detecting the substrate. The wettability angles displayed errors of 10.82 and 15.73% for the left and right angles, respectively. Interestingly, it proved common for analysis to return angles with significant error disparity, such as tracks 12, 16, 33, 41 and 50; a potential explanation is the fact that both angles are measured considering the substrate line, instead of taking into account a possible height difference between the left and right hand side of the beads. The area below the substrate \(A_{b}\) should also be improved, with tracks 7, 10, 32 and 50 displaying errors above 30%; in these cases, the area is considerably overestimated due to poor mapping of the sum of pixel values by column into the melting pool dimensions, stretching the area to the horizontal limits of the line bead, instead of the horizontal limits of the diluted zone. In short, the current function works under the assumption that the area under the substrate's width and the line bead's widths are the same, a consideration that should be avoided in future adaptations.