## 2 Materials and Methods  
### Design of Experiment  
To prepare for the preliminary experiment, a Taguchi L27 design of experiments (DoE) was utilized. This approach facilitates a thorough analysis of the impact of six control parameters on the response variables in an effective manner. The set-up and findings of this preliminary experiment are outlined in Table 1.  
The Taguchi L27 technique was selected for its capacity to concurrently handle several parameters, enabling full comprehension of the interactions and their impact on the outputs. Conducting this initial phase is crucial to verifying the appropriateness of the chosen parameters and their substantial influence on the response variables, such as the weight of the parts and energy usage.  
By utilizing the Taguchi L27 DoE, we could build these correlations, which would allow us to improve the upcoming tests and increase the accuracy of the predictive ANN model. The knowledge acquired from this initial investigation guided the adjustment of the control parameters, guaranteeing a strong and dependable dataset for the training and validation of the model.  
A Python program was developed to analyze the correlation between the control parameters and response variables, as depicted in Figure 1. For the weight response variable, almost all parameters exhibited correlations above 0.1 or below \(-0.1\), except for the injection pressure, which showed a correlation of \(-0.06\). Regarding energy consumption, the melt temperature was the dominant factor with a correlation of 0.73 followed by the screw speed. Other parameters demonstrated minimal correlation, including the injection pressure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Melt Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Mold Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Injection** \\ **Speed** \\ **(mm/s)** \\ \end{tabular} & \begin{tabular}{c} **Packing** \\ **Pressure** \\ **(bar)** \\ \end{tabular} & \
\begin{tabular}{c} **Screw** \\ **Speed** \\ **(rpm)** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Resumption (Wh)** \\ \end{tabular} \\ \hline MAX & 242 & 73 & 52 & 72 & 73 & 2.4929 & 44.9906 \\ MIN & 192 & 43 & 12 & 22 & 23 & 2.3379 & 24.5721 \\ AVE & 218.5 & 56.5 & 28.5 & 46 & 49 & 2.4003 & 28.7938 \\ ST. DEV & 18.56 & 10.52 & 13.38 & 16.87 & 18.53 & 0.0318 & 2.4262 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information for 486 point dataset.  
In addition to using the correlation matrix to validate the Taguchi results, we also conducted an analysis with Lasso feature importance and examined the response of the signal-to-noise ratio using Minitab 21. The Lasso method was chosen because it is capable of selecting influential features effectively even when observations are generated from an unknown nonlinear link function, while the learning process operates with a linear hypothesis class [24]. The results of these two methods are presented in Figure 2 and Tables 2 and 3. It can be observed from both methods that the injection pressure is the least influential feature for both responses.  
Figure 1: Correlation matrix from preliminary experiment.  
Figure 2: Features importance for weight and energy consumption.  
Based on the minimal correlation of the injection pressure with both response variables, it was decided to exclude the injection pressure from the control parameters. This decision was consistent with previous research [25], where it was found that the influence of the injection pressure on the part weight in polypropylene (PP) material increased linearly up to a certain pressure point, after which the part weight either stabilized or decreased.  
A full factorial design of experiments (DoE) was created using five control parameters for the polypropylene (PP) material, as shown in Table 4. The total number of runs required for a single set of the full factorial design was 243 in one experiment.