## 4 Conclusions  
This study successfully improved the weight of the product and reduced the energy consumption of the injection molding machine by integrating DoE with an ANN model and DE for parameter optimization. The predictive model exhibited exceptional performance throughout the training, validation, and test sets. The model demonstrated outstanding performance on the training set, with R\({}^{2}\) values of 99.16% for the weight and 95.99% for energy consumption. The combined R\({}^{2}\) total of 97.58% indicates a robust predictive capability. The model achieved a high R\({}^{2}\) of 98.95% for weight prediction on the validation set. However, the performance for energy consumption significantly declined to an R\({}^{2}\) value of 88.57%, resulting in a combined R\({}^{2}\) total of 93.76%. The model exhibited a decline in performance on the test set, as seen by R\({}^{2}\) values of 98.49% for the weight and 84.12% for energy consumption, resulting in an overall R\({}^{2}\) total of 91.31%. Although there was a decrease, the model nevertheless effectively accounted for a substantial amount of variability in both answers, indicating strong generalization capability. DE achieved a 2% reduction in weight and a 14% reduction in energy consumption. These findings demonstrate the effectiveness of integrating an ANN with DE for improving injection molding parameters, particularly in situations involving multi-objectives.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Original** & **Minitab** & **Diff** & **DE** & **Diff** \\ \hline
**Melt Temperature (\({}^{\circ}\)C)** & 220 & 217 & \(-1\)\% & 224 & 2\% \\
**Mold Temperature (\({}^{\circ}\)C)** & 55 & 58 & 5\% & 61 & 11\% \\ Injection Speed (mm/s) & 25 & 52 & 108\% & 29 & 16\% \\
**Packing Pressure (bar)** & 45 & 22 & \(-51\)\% & 22 & \(-51\)\% \\
**Screw Speed (rpm)** & 50 & 73 & 46\% & 39 & \(-22\)\% \\
**Weight (g)** & 2.3904 & 2.3593 & \(-1\)\% & 2.3441 & \(-2\)\% \\
**Energy Consumption (Wh)** & 28.5863 & 26.2660 & \(-8\)\% & 24.5721 & \(-14\)\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparation of optimization results.  
**Author Contributions:** Conceptualization, Y-H.T. and D.O.; methodology, Y-H.T., D.O. and S.; software, D.O.; validation, D.O.; investigation, D.O.; resources, Y.-H.T. and S.; data curation, D.O.; writing--original draft preparation, Y.-H.T. and D.O.; writing--review and editing, Y.-H.T. and S.; visualization, D.O.; supervision, Y.-H.T. and S.; project administration, Y.-H.T.; funding acquisition, Y.-H.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Since there will be further studies, in order to keep completion, the data will not be provided in this study.  
**Acknowledgments:** The authors would like to extend our deepest gratitude to the R&D Center for Smart Manufacturing of CYCU in Taiwan, for their invaluable support and for providing the facilities necessary to conduct this research. We are immensely grateful to all of the staff and lab technicians for their continuous assistance and support. Additionally, we would like to thank the Department of Mechanical Engineering of CYCU for its unwavering support and insightful discussions throughout the experimental phase. These contributions were instrumental in the successful completion of this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.