## 2 Materials and Methods  
Due to the large number of challenges faced by researchers, as listed in Section 1, it is necessary to determine a robust research methodology to help address these challenges. Specifically, to achieve a successful outcome, researchers have determined that the working methodology that could help is the well-known \"divide-and-conquer\" methodology. The main objective of this methodology is to simplify the resolution of the original problem. To achieve this, the general problem, defined as \(P\), is decomposed into smaller and more manageable subproblems where \(P=P_{1},P_{2},...,P_{n}\). Once each subproblem has been individually addressed and its specific solution obtained, defined as \(S_{i}=solve(P_{i})\), the partial results are systematically combined to construct the complete solution to the original problem, giving a \(S=C(S_{1},S_{2},\ldots,S_{n})=C(\operatorname{solve}(P_{1}),\operatorname{ solve}(P_{2}),\ldots,\operatorname{solve}(P_{n}))=C(\bigcup_{i=1}^{n} \operatorname{solve}(P_{i}))\), where \(C\) is the function that combines those partial solutions. This _divide-and-conquer_ strategy allows for a reduction in computational complexity and facilitates a structured and efficient problem-solving approach. Historically, this methodology is widely used to manage several kinds of challenges, for instance, to deal with legal challenges [17], mathematical calculations [18], and computational problems (accurately in parallel processing) [19]. Consequently, taking this idea into account, the steps defined in this work are detailed as follows (additional information is shown in Figure 1):  
1. Identification of the problem and challenges to overcome. The aim of this initial step is to establish the background and context of the problem to be solved. In other words, we work to understand what we are trying to solve. 2. Acquisition of knowledge. The second step involves acquiring high-level knowledge to provide the necessary overview for initiating the research. Then, when working on a more specific challenge, that topic will be studied in greater detail. 3. Challenge division. Based on the main idea of this methodology, the overall work is divided into several smaller challenges. In our case, the following challenges were detected: (i) Image typology classification to identify specific workflow. (ii) Automation of feature inference and extraction from non-etched metallographic microstructures. (iii) Enhanced feature extraction from chemically etched metallographic specimens, and (iv) functional integration and a user-centric interface for AI-driven metallographic analysis. For each of these, the following sub-phases will be performed: (a) Acquisition of specific knowledge. Once the topic is defined, it is time to expand the knowledge needed to solve the problem. (b) Definition of the experiment and the techniques to be used. The specific research and experiments are outlined for each of the challenges faced. (c) Evaluation. The defined experiment is carried out, and results are obtained for the approximation that has been defined. (d) Analysis. A specific process is performed on the data collected during the designed experiment. To support the current defined methodology and deal with the aforementioned detected challenges, a comprehensive dataset of metallographic images was compiled and processed. All metallographic images were extracted from several reports generated by the Azterlan Metallographic Department. More accurately, the dataset consists of a total of 2230 metallographic images, including various graphite morphologies and preparation conditions representative of real-world foundry scenarios. More specifically, the dataset was grouped into three primary graphite types, as follows: (i) lamellar (approximately 9.2% of images), (ii) vermicular (3.3%), and (iii) nodular (87.5%). Regarding the nodular subset, which is the main focus of this work due to its industrial relevance, the samples were further divided based on different characteristics.