## 1 Introduction  
Society has been evolving for years and increasing its population; thus, the demand for products is increasing. Specifically, many products need metal parts produced by the metalworking industry, which has evolved since the different metal ages in human history [1]. In order to make this type of production feasible, the industry realized that it must improve and optimize its production process. Currently, there are a large number of foundries that employ different production methods. For instance, there are foundries that work with green molding, chemical molding, or shell molding, among others. This type of manufacturing process dates back to ancient metalworking eras. In contrast, investment casting, although not widely adopted, is an industry with very high demand due to it being able to generate very sophisticated parts. As a result, it is a complex and expensive process. The investment casting process was created thousands of years ago in the Chinese empire. The main characteristic of this process is the possibility of creating very precise castings with difficult geometries to fill. Moreover, it is able to use a wide range of metallic alloys for manufacturing its parts compared with most popular foundry processes. Hence, this process is suitable for high added value and small series of highly demanding parts [2]. The main sectors addressed by this process are the aeronautic, automotive, and weapon-making. These types of industry are final users that require an additional degree of safety of their products [3]. These manufacturing processes, because of their nature, have a common challenge. They are very sensitive to variables related to the ceramic mold building up around the wax pattern, to the melting and pouring conditions, and in some cases, to the final heat treatment. In this way, the main problem is the presence of different defects on the final parts, like shrinkages and pores that require re-fixing of the component or even its final rejection [4]. Another problem is the process itself. The part is only visible at the end of the process, once finishing operations have been carried out. At that point, if problems are discovered, they are converted into significant loses to companies as non-quality costs. The most common strategy to address this problem is to understand the behavior of the process by developing process simulations based on physics concepts like fluid dynamics and heat transfer. These models are focused on forecasting the final performance of produced parts based on variables like the filling speed, heat transfer, and contraction during solidification. They can make predictions in terms of slag formation, shrinkage, or hot cracks, respectively. This kind of task makes the manufactured part more expensive. In addition, castings are affected by a variety of different factors; for example, the metal composition, mold design, and casting conditions, which can influence the final outcome [5]. Currently, the industry is seeking alternatives to avoid part defects [6], providing solutions that can predict the soundness of the parts. Moreover, during the last decade, significant effort was dedicated to improving final product quality, supported by the use of ICT, its integration into manufacturing equipment, and the application of the internet of things. For example, [PERSON] et al. [7] reviewed several studies in the literature that make use of artificial intelligence (AI) models to improve parts or processes. Thus, the authors collected information regarding several types of manufacturing processes; for example, sand processes and continuous processes, among others. They also discussed investment casting, describing how ANNs have been used to perform numerical simulations of different mold temperatures [8]. More accurately, they work to manage some aspects such as mold temperature, melting temperature, casting part material, number and location of feeding points, diameter and length of inflow channels, and the angle of the channel with respect to the main sprue axis.