## 4 Discussion and Conclusions  
The investment casting production process generates parts for industries such as aeronautics, where high levels of precision and quality are required. Thus, many research efforts focus on developing tools to mitigate these types of problems. To that end, this research aims to address two of the most critical defects. On the one hand, it focuses on mitigating the appearance of porosities. On the other hand, it works to ensure that elongation values remain within the predefined desired ranges. The contributions made by the research team to achieve the previously described objective are listed below. First of all, to achieve the aforementioned objectives, the research team worked on improving digitization and reality representation in a digital environment. Specifically, the tasks carried out were guided by a data analysis plan that included: (i) the identification of available data and data that, although initially unavailable, could later be digitized and extracted, (ii) an audit of the status of each area and its variables, (iii) a study of the impact and priority of the areas and their respective variables, and (iv) the creation of a centralized data repository for storing captured data. This last task was accomplished using an agent-based system such as PDManager. Later, all retrieved data should be exploited. Hence, the first step was to create a low-maturity-level digital twin focused on remote visualization and monitoring. To achieve this, we worked on defining what data should be displayed, when it should be displayed, to whom, and in what manner. This work was translated into the definition of the message communication protocol for information distribution, the design, and the creation of 25 different dashboard screens. Finally, these designed screens were installed in the plant to be useful for workers. Then, the digital twin was evolved to a higher degree of maturity to operate proactively rather than reactively. The new system integrates simulation and optimization tasks. The simulation, in other words, the predictions, was performed using machine learning. Specifically, supervised learning algorithms were used, as the data gathering process provided labeled output variables for the final prediction. In this research, an extensive analysis was conducted, first evaluating classical unary statistical classifiers and then applying meta-classification algorithms to mitigate the issues associated with traditional classifiers. The results achieved were consistent with the literature. In other words, meta-classifiers performed their predictions as effectively as unary classifiers, achieving accuracy percentages very close to classical methods while avoiding their inherent limitations. Regarding the optimization process, an advisor system was created using the extracted knowledge of the process. Part of that knowledge was manually gathered from workers and engineers, while the other part was automatically generated from the already collected data. This optimization tool is triggered when the aforementioned predictors detect a critical situation. Once the tool is launched, the calculation process begins to determine the best corrective action if needed, and finally, the recommended actions are communicated to the workers. Although the results obtained are very promising, we have identified some shortcomings, which are discussed below. * Amount of data: As the system was developed during this research, the datasets used for the artificial intelligence learning process were small. The ways to address this limitation are: (i) waiting for the dataset to grow as the foundry continues production or (ii) generating synthetic data using techniques such as Monte Carlo simulations [70] or the application of Generative Adversarial Networks (GANs) [71]. Hence, it is expected that when the data quantity increases, the prediction and optimization results will be better.