Aplicación de la visión por computadora en el análisis de microestructuras y la obtención de relaciones estructura - propiedad

Manual identification, classification, and segmentation of micrographs is a complex and time-consuming task, even for experts in materials science. With the advancement of computers as well as the emergence of increasingly accurate and robust machine learning algorithms, there has been a growing interest from the scientific community in introducing these new approaches to a science known to be highly empirical. In this context and in order to contribute to this topic, a supervised machine learning model was implemented in this study for the segmentation of pearlite and ferrite in hypoeutectoid steels. For its training, the texture of the regions of interest was used as the distinguishing feature between them. The trained algorithm was then applied to the validation and test data set, evaluating both its classification and segmentation performance, with statistical metrics commonly used in segmentation, such as the confusion matrix, F1-score and Jaccard index. In this way, a supervised machine learning model was obtained, capable of segmenting hypoeutectoid steels and calculating the volume fraction of the proeutectoid ferrite and pearlite. Later, in a post-processing step of the micrographs and using the segmentation produced by the model, it was possible to estimate the mean apparent interlaminar spacing of the pearlite, which together with the volume fractions of the phases present allowed to predict the mechanical properties of the material associated with the micrograph. These predictions were compared with values reported by the ASM for some micrographs and it was found that, in general, they were very consistent. The final performance of the model was 75.7% in Micro Averaged Jaccard Index; and 85.3% in accuracy. These values are comparable with those reported by segmentation models reviewed in the literature.

• Autores: Martínez Martínez, Camilo Andrés
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2021
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: spa
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/51616
• Acceso en línea: http://hdl.handle.net/1992/51616
• Palabra clave: Acero al carbono; Aprendizaje automático (Inteligencia artificial); Micrografía; Ciencia de los materiales; Ingeniería
• Rights: openAccess
• License: https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf