An objective metallographic analysis approach based on advanced image processing techniques

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dc.contributor.authorSarrionandia, Xabier
dc.contributor.authorNieves Acedo, Javier
dc.contributor.authorBravo, Beñat
dc.contributor.authorPastor López, Iker
dc.contributor.authorGarcía Bringas, Pablo
dc.date.accessioned2025-07-10T08:11:10Z
dc.date.available2025-07-10T08:11:10Z
dc.date.issued2023-01-04
dc.date.updated2025-07-10T08:11:10Z
dc.description.abstractMetallographic analyses of nodular iron casting methods are based on visual comparisons according to measuring standards. Specifically, the microstructure is analyzed in a subjective manner by comparing the extracted image from the microscope to pre-defined image templates. The achieved classifications can be confused, due to the fact that the features extracted by a human being could be interpreted differently depending on many variables, such as the conditions of the observer. In particular, this kind of problem represents an uncertainty when classifying metallic properties, which can influence the integrity of castings that play critical roles in safety devices or structures. Although there are existing solutions working with extracted images and applying some computer vision techniques to manage the measurements of the microstructure, those results are not too accurate. In fact, they are not able to characterize all specific features of the image and, they cannot be adapted to several characterization methods depending on the specific regulation or customer. Hence, in order to solve this problem, we propose a framework to improve and automatize the evaluations by combining classical machine vision techniques for feature extraction and deep learning technologies, to objectively make classifications. To adapt to the real analysis environments, all included inputs in our models were gathered directly from the historical repository of metallurgy from the Azterlan Research Centre (labeled using expert knowledge from engineers). The proposed approach concludes that these techniques (a classification under a pipeline of deep neural networks and the quality classification using an ANN classifier) are viable to carry out the extraction and classification of metallographic features with great accuracy and time, and it is possible to deploy software with the models to work on real-time situations. Moreover, this method provides a direct way to classify the metallurgical quality of the molten metal, allowing us to determine the possible behaviors of the final produced parts.en
dc.identifier.citationSarrionandia, X., Nieves, J., Bravo, B., Pastor-López, I., & Bringas, P. G. (2023). An objective metallographic analysis approach based on advanced image processing techniques. Journal of Manufacturing and Materials Processing, 7(1). https://doi.org/10.3390/JMMP7010017
dc.identifier.doi10.3390/JMMP7010017
dc.identifier.eissn2504-4494
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3188
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2023 by the authors
dc.subject.otherArtificial vision
dc.subject.otherClassification
dc.subject.otherDeep learning
dc.subject.otherMachine learning
dc.subject.otherMetallography
dc.titleAn objective metallographic analysis approach based on advanced image processing techniquesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleJournal of Manufacturing and Materials Processing
oaire.citation.volume7
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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