## 6 Discussion and Conclusions  
In this study, artificial neural networks (ANNs) were built to predict the relationship between process conditions and product quality in injection molding. Injection molding experiments were conducted on bowl products, and data were collected to evaluate the predictive performance of a multi-task learning structure with the grouping of quality factors based on their physical meanings and characteristics.  
Based on the collected dataset, three different ANN networks with different architectures were constructed. One is the Network A architecture, which is the existing multi-input, multi-output (MIMO) structure, where the output parameters for them ass, diameter, and height of the molded product are connected to a single task layer. Another is Network B, where diameter and height are grouped and assigned to one task layer, and mass is separated into a separate group with its own task layer. The last is Network C, where all output parameters, mass, diameter, and height, are grouped separately with individual  
Figure 13: Performances of the prediction models using test data according to networks in terms of (a) mass; (b) diameter; (c) height.  
task layers. In the case of Networks B and C, which applied multi-task learning according to output parameter groups, both showed relatively superior performance in predicting product quality in all scenarios compared to that of the typical MIMO-ANN, Network A. In particular, the architecture of Network 3, which assigns product mass, diameter, and height to separate task groups, showed excellent performance in predicting product weight, diameter, and length. When compared to the RMSE value of the general MIMO-ANN, Network A, the overall root mean square error (RMSE) for Network C on the entire test data showed an improvement of approximately 32.8%. For mass, diameter, and height, the respective improvements were 56.6%, 15.0%, and 44.3%, indicating that Network C exhibited superior predictive performance compared to that of the conventional MIMO neural network (Network A) based on RMSE. These results suggest that a multi-task learning architecture, which separates groups based on the characteristics of the quality factors of injection-molded products set as output parameters, may be a more suitable approach for the quality prediction of injection-molded products using the ANN.  
The analysis of the specific dataset of the bowl product used in this study indicates that a multi-task learning architecture, which divides and assigns separate tasks based on the physical meanings and characteristics of the quality factors of injection molded products set as output parameters, may be a better choice for predicting the mass, diameter, and height of injection-molded products compared to the conventional MIMO structure of the ANN. The results of this study are expected to serve as valuable reference material for future research on the application of the ANN in the injection molding industry.  
Conceptualization, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); data curation, [PERSON]; formal analysis, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); validation, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]); visualization, [PERSON]; investigators, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Trade, Industry and Energy, and the Korea Evaluation Institute of Industrial Technology (KEIT) in 2023 (20019131, KM230100).  
Not applicable.  
The data presented in this study are openly available at [[https://doi.org/10.3390/polym14091724](https://doi.org/10.3390/polym14091724)]([https://doi.org/10.3390/polym14091724](https://doi.org/10.3390/polym14091724)), reference number [17].  
The authors declare no conflict of interest.