## 5 Conclusions  
This study presents a comprehensive analysis of the plastic injection molding process, focusing on optimization through a combined approach of a precise classification model, a reasoning-capable intelligent agent, and a user-friendly graphical user interface (GUI). The obtained results validate the feasibility of the proposed approach, highlighting its potential to significantly improve process performance in terms of the following:  
1. Productivity: Optimizing the cycle time (CT) as the primary control variable reduces the total cycle time, increasing the number of parts produced per unit of time and, consequently, the overall process productivity.
2. Product Quality: An optimized CT ensures precise control of the injection, solidification, and material ejection conditions, minimizing defects and guaranteeing high-quality final products.
3. Energy Efficiency: Process optimization reduces unnecessary energy consumption, decreasing operating costs and contributing to environmental sustainability.
4. Proactive Decision Making: The classification model, by predicting CT behavior, enables the anticipation of potential deviations and the implementation of preventive measures to avoid process interruptions or failures.
5. Adaptability and Scalability: The proposed approach is highly flexible, adapting to different machine configurations, materials, and mold geometries. Additionally, its scalability potential allows for application in industrial processes of varying complexity.
6. Industrial Impact: The successful implementation of this approach in the manufacturing industry can significantly impact the efficiency, quality, and sustainability of production processes, contributing to sector competitiveness and the development of innovative products.  
This study has demonstrated the value of an integrated approach combining a classification model, an intelligent agent, and a GUI for optimizing the plastic injection molding process. The method applied to plastic injection molding can be applied to plastic extrusion, casting, forging, baking in ovens, and others, since the variables that were taken into account in the generated models are involved in them.  
The obtained results and future research opportunities open a promising avenue for continuous improvement in efficiency, quality, and sustainability within the manufacturing industry. This work is expected to contribute significantly to technological advancements and the development of more efficient and environmentally friendly industrial processes.  
Conceptualization, J.E.S.-S., J.I.H.-V. and C.H.-S.; methodology, L.A.R.-G., M.C.G.-M. and M.E.M.-A.; software, J.E.S.-S. and A.H.; validation, N.d.I.C., J.E.S.-S., J.I.H.-V. and A.H.; formal analysis, M.C.G.-M., M.E.M.-A. and L.A.R.-G.; investigation, J.E.S.-S. and N.d.I.C.; resources, J.I.H.-V., M.E.M.-A. and C.H.-S.; data curation, J.E.S.-S. and J.I.H.-V.; writing--original draft preparation, J.E.S.-S. and C.H.-S.; writing--review and editing, L.A.R.-G., M.C.G.-M., M.E.M.-A., A.H. and N.d.I.C.; visualization, A.H. and J.I.H.-V.; supervision, C.H.-S. and J.I.H.-V.; project administration, J.E.S.-S. and C.H.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.