### Bibliographic Review  
Research has been conducted demonstrating the benefits of artificial intelligence in the field of materials to develop expert systems focused on data analysis and failure diagnosis [7; 8; 9]. Artificial neural networks have been present in stainless steel to solve classification, recognition, and estimation tasks. In the work shown by [PERSON] et al. [10], the wear rate of 316L stainless steel was predicted by using artificial neural networks. The results showed that the CrN and \(\gamma^{\prime}\)-Fe4N phases of the steel are present at temperatures above 400 \({}^{\circ}\)C, while the S-phase formed at temperatures below 400 \({}^{\circ}\)C.  
In the research developed by [PERSON] et al. [11], the mechanical properties of 304L and 326L austenitic stainless steels subjected to elevated temperatures were studied using artificial neural networks trained to predict different mechanical properties, such as the elongation and strength coefficient. [PERSON] et al. [12] demonstrate the efficiency of an artificial neural network to predict the flow stress of austenitic stainless steel 304 during its heat deformation from the perspective of the following variables: voltage and temperature. The work presented by [PERSON] et al. [13] addresses the implementation of deep learning technologies to predict the tribological characteristics of 316 stainless steels versus a 100 Cr6 alloy. The coefficient data and friction forces from the experiments were used to develop the different deep learning models, and the performance of the learning model demonstrated higher levels of accuracy. In [PERSON] et al. [14], the development of a system based on an artificial neural network to detect cracks in steel and estimate their depth from 2D images was reported. Automatic crack detection is needed to reduce costs and improve the quality of surface inspection for infrastructure maintenance. The performance of the system is comparable to the state of the art and provides an applicable and affordable inspection device. The research by [PERSON] et al. [15] shows the development of an automated method for damage localization in stainless steel structures using acoustic emission signals and a convolutional neural network based on weighted ensemble regression. A data fusion approach was designed to integrate the waveform information into three types of images. A convolutional neural network supported by weighted ensemble regression was proposed to analyze the images and calculate the damage coordinates. In the area of study related to stainless steel, artificial neural networks have covered tasks such as wear estimation, crack detection, and damage localization generated in stainless steel. Regarding the integration of digital image processing and artificial neural networks applied to the recognition of wear patterns generated in metallographic images, the following works have been presented: In [PERSON] et al. [16], the classification of machined surfaces was performed using image processing and machine learning techniques. The images were preprocessed to extract GLCM-based features and sent to a classifier such as a support vector machine. The research by [PERSON] et al. [17] analyzes the corrosion inhibition efficiency of carbon steel specimens based on images characterized using a gray-level co-occurrence matrix (GLCM) and a support vector machine. [PERSON] and [PERSON] [18] employ phase congruency processing to visualize the extent of subsurface damage in aluminum structures. [PERSON] et al. [19] used binarization-based digital image processing from illumination and contrast invariant measurements to quantify the porosity of AISI 316L stainless steel. [PERSON] and [PERSON] [20] applied the principles of phase congruence by implementing Fourier transform on turned surfaces to identify their wear. As can be seen, the main digital image processing techniques used for the characterization of microstructures in steels that have suffered different types of damage are phase congruence and feature extraction based on GLCMs.