## 3 Results and Discussion
### Feature Analysis  
In Figures 4-6 a visual analysis of the parameters is observed during a build process, while this information is important during in situ monitoring, a statistical/qualitative analysis is very important to evaluate the parameters of the build process. Therefore, in the following section an in-depth analysis explores parameters collected from sample 2 while it was built.  
Considering the 5 most impactful features during the build process, this section is dedicated to analyzing and visualizing the most important features through the building of sample 2. Figure 12 displays the LP, MPS, and SLED features throughout the build process for sample 2. The base of sample 2 consists of the first 6 layers, this is noticeable in the  
Figure 11: Heat map comparison of all features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Decision Tree** & **Random Forest** & **XGBoost** \\ \hline ContourFlag & 0 & 0.009259 & 0.454528 \\ FeedrateVel & 0.027871 & 0.033033 & 0.05253 \\ LP & 0.203724 & 0.190519 & 0.095578 \\ MeltPoolSize & 0.277692 & 0.181422 & 0.063341 \\ SLED & 0.106971 & 0.177238 & 0.059055 \\ PowderDiscSpeed & 0.036366 & 0.039298 & 0.119394 \\ ShieldGas & 0.117805 & 0.113374 & 0.056001 \\ PowderCarrierGas & 0.113309 & 0.121429 & 0.047792 \\ EnclosureTemp & 0.116262 & 0.134427 & 0.05178 \\ LaserSpotSize & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of feature selection techniques.  
dataset. Layers 1 through 6 consist of at least 155 to 174 data points while layers 7 through 284 all contain under 20 data points. Through this logic, we define the base as layers 1 through 6 and the shaft as layers 7 through 284. The base of all three visualizations in Figure 12, circled in red, demonstrates a distinct grouping of measurements when compared to the shaft portion of the sample.  
Table 4 showcases a value comparison of the 5 most impactful features for classification between the base and shaft. This table demonstrates that the base experiences a significantly higher LP usage and smaller overall MPS. Variations in LP, MPS, and SLED are higher when building the base and have less deviation through the building of the shaft. The largest MPS occurs when building the shaft with a value of 1.989 mm. The small amount of variation in the features when building the shaft may be responsible for over-fitting of the CNN model. Evaluation of this topic is explored deeper in Section 3.2.1.  
Table 5 separates the features based on labels in an effort to describe the values of the features based on layer quality. Average wattage and variance for LP are higher in layers deemed acceptable. The highest recorded LP measure occurs within an unacceptable layer. The sizes of melt pools on average are smaller (0.033 mm) for acceptable layers however, the largest MPS occurs within an acceptable layer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Base**} \\ \hline  & **Layer** & **LP [W]** & **MPS [mm]** & **SLED [J/mm\({}^{2}\)]** & **PCG [L/min]** & **ET [C]** \\ \hline count & 1006 & 1006 & 1006 & 1006 & 1006 & 1006 \\ mean & - & 362.6 & 0.916 & 0.302 & 6.978 & 28.96 \\ std & - & 57.84 & 0.356 & 0.048 & 0.007 & 0.163 \\ min & 1 & 0 & 0 & 0 & 6.958 & 28.68 \\ max & 6 & 414.9 & 1.518 & 0.413 & 6.999 & 29.17 \\ \hline  & \multicolumn{4}{c}{Shaft} \\ \hline count & 3944 & 3944 & 3944 & 3944 & 3944 & 3944 \\ mean & - & 232.6 & 1.178 & 0.198 & 6.978 & 29.02 \\ std & - & 38.05 & 0.257 & 0.034 & 0.007 & 0.165 \\ min & 7 & 0 & 0 & 0 & 6.953 & 28.67 \\ max & 284 & 487.5 & 1.989 & 0.416 & 6.999 & 29.26 \\ Importance & & 0.191 & 0.181 & 0.177 & 0.121 & 0.134 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of feature throughout build.  
Figure 12: Visualization of features of build of **(a)** LP, **(b)** MPS, and **(c)** SLED.