## 3 Results
### Functional Integration  
Taking into account the complexity of the workflow defined and explained in the Materials and Methods section--which involves several DL classifiers, CV-based feature extractors, and other elements for image detection and segmentation--the subjective approach to metallographic image analysis is not easily accessible to the average foundry user. Thus, one of the identified challenges involves the integration of all these tools into a disruptive system that consolidates their operations. This system, called HawkEye (in Figure 19), is a specific software for objective nodular metallography analysis supported by our designed AI-driven pipeline. After executing the workflow on the images loaded into the HawkEye software, information regarding the type of metallography, quality assessment, and the distribution  
Figure 19: HawkEye software startup and manual configuration for metallographic image analysis. Figure 18: Processed metallography showing the pixel detection of carbides using SAM after the detection of the bounding boxes. and typology of particles is displayed. An image of the detected particles, color-coded by type, is also shown, along with a list of the particles and their properties (see Figure 20). The output generated by the system is considered suitable for formal reporting purposes. Every report must list the following: sample ID and extraction location; the number of samples and areas evaluated; graphite morphology, distribution, and size; magnification level; relevant applicable standard; report date; responsible organization; and the name and signature of the author. If required, the report may also include additional details, such as the graphite nodule count; material designation; details of the metallographic preparation procedure; dimensions of the extracted sample or the wall thickness in cases where the sample is taken from a casting; and an additional graphite size assessment where applicable. Similarly, as the system is also configured to analyze etched metallographic samples, it is fully capable of processing and interpreting this type of image. Figure 21 illustrates the analysis of an etched microstructure, where the segmentation and quantification of ferrite, perlite, and carbide are visualized. As with the previous non-etched samples, the corresponding information can be automatically compiled into a standardized report format, ensuring consistency in documentation and full traceability across evaluations. Figure 20: Examples of metallographic images processed within the workflow. The last images show the respective list of particles detected. An additional feature of the system is its ability to log all historical data, store it in a dedicated database, and correlate these analytical results with other variables from the production process. This functionality enables the creation of high-value datasets suitable for machine learning and artificial intelligence models, while also supporting the enrichment of digital material passports with detailed microstructural information and the related metadata. Once all AI- and VC-based tools were integrated into our pipeline and the HawkEye software was created, we proceeded to evaluate its performance. Specifically, we compared it with other initial scripts developed by Azterlan researchers, as well as manual categorizations and classifications performed by metallography experts. Hence, we can conclude the following. The software demonstrates a good level of accuracy in the analysis of metallographic images, particularly in identifying globular structures and homogeneous phases such as ferrite. However, there are notable discrepancies when analyzing more complex microstructural features. In non-etched (nodular) images, the deviation is minimal, with 0% deviation in modularity, 1% in nodule-type identification for the CV method, 5% in ML, 4% in quality assessment in ML, and 1% in the curve-based analysis, indicating a high degree of reliability in basic morphological classification.