## 5 Results  
Table 3 shows the results of hyperparameter exploration for Networks A, B, and C. It is important to note that during hyperparameter exploration, the hidden layer structure was kept consistent across the three different artificial neural network (ANN) architectures to facilitate intuitive comparison. The prediction results for the untrained test data (Experiments #28, 30, 31, 32, 36, and 45, as shown in Table A1) using the neural network described in Table 3 are shown in Table 4. To evaluate the performance, the root mean square error (RMSE) between the measured values and the predictions generated by the neural network was calculated for the normalized test data. As shown in Table 4, the application of grouping for the quality factors of injection-molded products resulted in superior performance for Network B and C, which used the multi-task learning structure, compared to that of the conventional single MIMO structure used in Network A. In particular, Network C exhibited the best RMSE value, showing an \begin{table}
\begin{tabular}{c c} \hline
**Network** & **Total Normalized Test Data** \\ \hline A & \(6.162\times 10^{-2}\) \\ B & \(5.280\times 10^{-2}\) \\ C & \(4.142\times 10^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Root mean square errors (RMSEs) of total normalized property data for networks. \begin{table}
\begin{tabular}{c c c} \hline
**Hyperparameters** & **Range** & **Note** \\ \hline Seed number & 0–50 & Step size was 1 \\ Batch size & 16, 32, 64, \(\ldots\) & Increased in multiples of 2 until it could \\  & & cover the number of learning data \\ Optimizer & Adams [15] & Fixed \\ Learning rate & 0.0001–0.01 [15] & Step size was 0.0001 \\ Beta 1 & 0.1–1.0 [15] & Step size was 0.1 \\ Data 2 & 0.9, 0.99, 0.999, 0.999 [15] & - \\ Number of neurons & 3–18 & Step size was 1 \\ Initialize & He normal (hidden layer) & - \\  & Xavier normal (output layer) & - \\ Activation function & Elu (hidden layer) & - \\  & Linear (output layer) & - \\ Drop number & 0.0–0.4 & Step size was 0.1 \\ Coefficient of L2 normalization & 0.001, 0.01, 0.1 & - \\ \hline \end{tabular}
\end{table} 
Table 2: Ranges of hyperparameters for networks. \begin{table}
\begin{tabular}{c c c c} \hline
**Hyperparameters** & **Network A** & **Network B** & **Network C** \\ \hline Seed number & 17 & 6 & 47 \\ Batch size & 16 & 16 & 32 \\ Optimizer & 0.0073 & 0.0051 & 0.0052 \\ \begin{tabular}{c} Beta 1 \\ Data 2 \\ \end{tabular} & 0.6 & 0.3 & 0.1 \\ \
\begin{tabular}{c} Beta 2 \\ \end{tabular} & 0.99 & 0.99 & 0.99 \\ Number of neurons & 17-13-8-7 & 17-15-13 (common layers) & 18-7-7 (common layers) \\
\begin{tabular}{c} [5, 7] \\ \end{tabular} (mass, length layer) & [3, 5, 6] (mass, diameter, height layer) \\ Initialize & & & \\ Activation function & & & \\ Drop number & 0.0-0.3-0.1-0.1 & 0.03-0.00-0.00 (common layer) & 0.0-0.2-0.0 (common layers) \\  & & [0.2, 0.0] (mass, length layer) & [0.1, 0.4, 0.2] (mass, diameter, height layers) \\ Coefficient of L2 normalization & 0.01 & 0.001, 0.001 (mass, length) & 0.01, 0.001, 0.1 (mass, diameter, height) \\ \hline \end{tabular}
\end{table} 
Table 3: Determined hyperparameters for networks. improvement of approximately 32.8% over that of the conventional structure of Network A in the total normalized test data. Table 5 shows the results comparing the prediction performance for each quality of the molded product, focusing on individual quality factors rather than the entire test dataset. Figure 11 graphically illustrates these results. In Table 5, it can be observed that Network A, the conventional MIMO neural network structure, exhibited the lowest prediction performance for all three factors: mass, diameter, and height. On the other hand, Network C showed the best prediction performance compared to that of Network A for mass, diameter, and height. It showed the most significant improvement in mass, achieving approximately 56.6% better performance based on the RMSE values.