Metallographic analysis using machine vision: a systematic review of the literature
Abstract
Metallographic analysis is a technique that allows studying the microstructure of metals and their alloys, related to their chemical and mechanical properties. Machine vision is a discipline that uses computational processes to extract information from images. This article presents a systematic review of the literature on the use of artificial vision for metallographic analysis, following the PRISMA methodology. We identified 50 relevant articles, published between 2018 and 2023, that address different aspects of metallographic analysis, such as defect detection, phase classification, grain size measurement, the characterisation of inclusions and the evaluation of surface quality. The methods, techniques and results of the articles were analyzed, as well as the challenges and opportunities for future research. It is concluded that machine vision is a useful and promising tool for metallographic analysis, which offers advantages such as automation, precision, speed and cost reduction.
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References
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