## 4 Discussion and Conclusions
In fact, as is described in [72], as data grow and evolve, a methodology must be implemented to ensure that machine learning models are developed, tested, and deployed in a consistent and reliable manner. Currently, this methodology is not yet defined, making it one of the key areas for future work. * Accuracy of prediction models: The accuracy achieved by the models was not high. First, the problem may be related to the volume of the datasets. This has already been discussed in the previous point. On the other hand, accuracy may be improved by using newer models, such as deep neural networks [73]. Therefore, future work should focus on identifying better machine learning models capable of representing the manufacturing process more accurately. Moreover, readers must consider that achieving a high accuracy level in machine learning models is subjective. In fact, while accuracy measurements between 70% and 90% may not be ideal, they are realistic, as the current process and workers are not capable of consistently reaching such levels of precision. * Simple optimization algorithms: Optimization and planning problems are among the most complex in the field of artificial intelligence. For this reason, and given that this research focused on developing an initial prototype, we decided to implement a simple recommendation system based on basic algorithms. However, incorporating new algorithms [74] could enhance the feedback provided to the plant. Additionally, if a computational bottleneck is detected, it may be possible to develop a hybrid system capable of executing quantum optimization algorithms [75] and subsequently processing them using conventional programming techniques. * Reduced optimization objectives: This work was done to optimize a defect and a mechanical property. However, there are many more defects and many more mechanical properties [5] that should be added in a further research. * Adaptation to new production trends and changes in manufacturing parameters: Classification systems may become obsolete over time due to changes in manufacturing processes. This is particularly relevant when systems must quickly adapt to different types of data and new castings. The main limitation of traditional classifiers is that they cannot autonomously adjust when receiving new information. To address this challenge, an approach based on continuous learning is proposed, where old models are replaced by new ones, iteratively adapting to changes. A key technique for this process is \"sample aging of data\" (as a fading factor), which assigns greater weight to new evidence, compensating for the reduced representativeness of older samples. One possible approach is the use of Bayesian compression and Monte Carlo methods to integrate old and new data, enabling the rapid development of new models that achieve higher accuracy. This process facilitates continuous updates and improves state prediction in dynamic systems [76; 77]. On the other hand, techniques such as reinforcement learning could provide a solution by adjusting models based on real outcomes observed during the production process [78]. The problems listed and the possible solutions described define the future work to be developed in order to ultimately achieve the most advanced digital twin possible. The desired solution will not only address the issues identified in this research but also many other challenges present in the production process. **Author Contributions:** Conceptualization, D.G., J.N. and P.P.R. ; methodology, J.N. ; software, J.N. and J.A.-P.; validation, D.G., J.N. and P.P.R. ; investigation, D.G., J.N., J.A.-P. and F.S. ; data curation, D.G. and P.P.R. ; writing--original draft preparation, J.N., D.G., J.A.-P. and P.P.R. ; writing--review and editing, J.N. and D.G. ; project administration, F.S. and P.P.R. ; funding acquisition, F.S. and P.P.R. All authors have read and agreed to the published version of the manuscript.