## 2 Materials and Methods  
Therefore, in an attempt to solve the already defined problem in Section 1, as well as the difficulty of addressing the process as a whole, researchers have proposed the use of the well-known \"Divide and Conquer\" (_divide et impera_) methodology. More accurately, this methodology tries to divide the general problem into smaller ones. In other words, it tries to transform the original problem into simpler ones that are easier to solve. Thus, once a solution has been found to solve each of them, the combination of all achieved developments solves the general problem. This methodology is widely used to deal with legal challenges [19], mathematical calculations [20], and computational problems (specifically in parallel processing) [21]. Consequently, building on this idea, the steps defined in this work are detailed below (additional information is shown in Figure 1). 1. Identification of the problem and the challenges to overcome: The goal of this initial step is to identify the background and context of the original problem. In essence, we focus on gaining a clear understanding of what we are aiming to solve. 2. Acquisition of knowledge: Collecting high-level knowledge provides the necessary overview to start the research. Nevertheless, later, when we focus on a more specific aspect of this challenge, this step will be repeated to ensure greater accuracy in the new generation of specific domain solutions. 3. Division of challenges: Starting from the idea of divisions mentioned earlier, we will define the challenges to address and the specific steps for each one. For this research, the following challenges have been identified: (i) digitization and manufacturing process representation, (ii) creation of a proactive system based on predictions, and (iii) retro-feedback for controlling and adjusting the manufacturing parameters. For each of them, the following sub-phases will be performed: a Acquisition of specific knowledge: Once the topic is defined, this stage involves expanding the knowledge necessary to address the problem. Frequently, this knowledge acquisition is closely linked to the exploration and understanding of the production process being optimized. b Definition of the experiment and the techniques to be used: At this point, the specific research and experiments are outlined for each of the challenges to be addressed. c Evaluation: At this stage, the defined experiments are conducted, and results are obtained for the approximation that has been defined. d Analysis: Once the previous stage is complete, the collected data from the experiments are analyzed. e Interpretation of the results: When each solution has been determined for all identified challenges, we will combine them. Any type of optimization of the manufacturing process, such as the manufacturing of castings using an investment casting process, involves exhaustive control of all associated operations. The more control there is and the fewer fluctuations that occur, the more reliable the results of the process. In this manner, the optimization and stabilization of the entire manufacturing process must be supported by Information and Communication Technologies (ICT). Thus, according to [PERSON] et al. (2012), the main goals are as follows:  
Figure 1: Research methodology and explanation of AI-based digital twin system. Digitization of the process: It is fundamental and necessary to digitally represent the manufacturing process. Expand the amount of digitized information, achieving a representative set that facilitates the understanding and management of the process. Generate a robust IIoT (Industrial Internet of Things) system designed to correctly define the appropriate ICT solution. Provide a solution that, in addition to describing the current behavior, can provide digitally calculated solutions that improve the real system.