An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques
Metallographic 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 classificati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/7/1/17 |
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| author | Xabier Sarrionandia Javier Nieves Beñat Bravo Iker Pastor-López Pablo G. Bringas |
| author_facet | Xabier Sarrionandia Javier Nieves Beñat Bravo Iker Pastor-López Pablo G. Bringas |
| author_sort | Xabier Sarrionandia |
| collection | DOAJ |
| description | Metallographic 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. |
| first_indexed | 2024-03-11T08:36:50Z |
| format | Article |
| id | doaj.art-cf5d6dad72144986bc70a93d3a6a6185 |
| institution | Directory Open Access Journal |
| issn | 2504-4494 |
| language | English |
| last_indexed | 2024-03-11T08:36:50Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj.art-cf5d6dad72144986bc70a93d3a6a61852023-11-16T21:25:51ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942023-01-01711710.3390/jmmp7010017An Objective Metallographic Analysis Approach Based on Advanced Image Processing TechniquesXabier Sarrionandia0Javier Nieves1Beñat Bravo2Iker Pastor-López3Pablo G. Bringas4AZTERLAN, Basque Research and Technology Alliance (BRTA), Aliendale Auzunea 6, 48200 Durango, SpainAZTERLAN, Basque Research and Technology Alliance (BRTA), Aliendale Auzunea 6, 48200 Durango, SpainAZTERLAN, Basque Research and Technology Alliance (BRTA), Aliendale Auzunea 6, 48200 Durango, SpainDepartment of Mechanics, Design and Industrial Management, Faculty of Engineering, University of Deusto, Unibertsitate Etorbidea 24, 48007 Bilbao, SpainDepartment of Mechanics, Design and Industrial Management, Faculty of Engineering, University of Deusto, Unibertsitate Etorbidea 24, 48007 Bilbao, SpainMetallographic 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.https://www.mdpi.com/2504-4494/7/1/17artificial visionmachine learningdeep learningmetallographyclassification |
| spellingShingle | Xabier Sarrionandia Javier Nieves Beñat Bravo Iker Pastor-López Pablo G. Bringas An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques Journal of Manufacturing and Materials Processing artificial vision machine learning deep learning metallography classification |
| title | An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques |
| title_full | An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques |
| title_fullStr | An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques |
| title_full_unstemmed | An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques |
| title_short | An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques |
| title_sort | objective metallographic analysis approach based on advanced image processing techniques |
| topic | artificial vision machine learning deep learning metallography classification |
| url | https://www.mdpi.com/2504-4494/7/1/17 |
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