## 3 Results and Discussion
### Cnn
#### 3.2.1 Evaluation of Over-Fitting  
While it is advantageous to have a classification model equipped with the ability to perform high-level classifications with raw AM process parameter inputs, the current form of the algorithm is not without limitations. DL algorithms become highly effective at prediction and classification as datasets become larger. When the training data is smaller, DL algorithms can result in issues of overfitting. This occurred during the training process of both samples. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline SVM (linear) & 0.90 \\ SVM (poly) & 0.90 \\ SVM (rbf) & 0.90 \\ SVM (sigmoid) & 0.84 \\ RF & 0.89 \\ Keras Classifier & 0.84 \\ CNN & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Acceptable Layers**} \\ \hline  & **LP [W]** & **MPS [mm]** & **SLED [J/mm\({}^{2}\)]** & **PCG [L/min]** & **ET [C]** \\ \hline count & 4453 & 4453 & 4453 & 4453 & 4453 \\ mean & 260.8 & 1.122 & 0.220 & 6.978 & 29.01 \\ std & 69.01 & 0.301 & 0.057 & 0.007 & 0.167 \\ min & 0 & 0 & 0 & 6.953 & 28.67 \\ max & 414.9 & 1.989 & 0.416 & 6.999 & 29.26 \\ \hline  & \multicolumn{5}{c}{Unacceptable Layers} \\ \hline count & 497 & 497 & 497 & 497 & 497 \\ mean & 242.6 & 1.154 & 0.206 & 6.978 & 29.03 \\ std & 50.76 & 0.283 & 0.044 & 0.007 & 0.159 \\ min & 0 & 0 & 0 & 6.955 & 28.68 \\ max & 487.5 & 1.670 & 0.405 & 6.997 & 29.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of features based on layer quality. In Figures 13a,b, we showcase how the CNN trains through a total of 100 epochs. Seen in Figure 13, training accuracy trends in an increasing fashion with some fluctuation throughout the process. The validation measure is seen to converge on training around the 62 nd epoch. Upon that, training and validation results appear to diverge as epochs increase. Looking at Figure 13b the overall loss plot is displayed throughout the training process. It can be seen around the 62 nd epoch that validation loss crosses the training loss threshold which corresponds with the'shuttlewise' movement in validation accuracy after the 62 nd epoch from the accuracy plot. Every additional epoch shows that the validation loss increases which indicates over-fitting is occurring during training. This issue is evaluated further in Section 3.2.1. The results from Section 3.2 in conjunction with the training plots indicate that the CNN trained and tested on data from sample 2 experienced over-fitting even when building the model using the dropout technique. The remainder of this subsection explores the results gathered while training and testing on both sample 2 (acceptable sample) and sample 1 (unacceptable sample). Over-fitting occurs in DL when the model becomes oversaturated (or over-dependent) from the training dataset and learn incorrect mapping [114; 115]. To fix this issue, some research has focused on L2 regularization. In [115], an adjustment to the Loss (or Cost) Function is made as:  
\[L^{*}(d,p,\theta)=L(d,p)+\lambda\sum_{i}\theta_{i}^{2} \tag{12}\]  
where \(L^{*}\) is the modified Loss function, \(L\) is the Loss (or Cost) function from the DL model. \(\lambda\) is a hyperparameter that adjusts the strength of the regularization and \(\theta\) is the updating parameter in the stochastic gradient descent (SGD) optimization equation, while there are many methods to solve the case of over-fitting, this study is focused on reporting results of CNN classification at the inter-layer level and comparing these results with other ML techniques. Figures 14a,b show the acceptable and unacceptable layers throughout each layer during the build process of sample 1. Figure 14a shows layer classification based on the size of low-density occurrences and Figure 14b shows the quantity of low-density occurrences for each layer of sample 1. It can be seen in these two figures that the majority  
Figure 13: CNN training plots of (**a**) accuracy and (**b**) loss.