A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones
The mechanical performance of metallic components is intrinsically linked to their microstructural features. However, the manual quantification of microconstituents in metallographic images remains a time-consuming and subjective task, often requiring over 15 min per image by a trained expert. To ad...
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2025-05-01
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| author | Cássio Danelon de Almeida Thales Tozatto Filgueiras Moisés Luiz Lagares Bruno da Silva Macêdo Camila Martins Saporetti Matteo Bodini Leonardo Goliatt |
| author_facet | Cássio Danelon de Almeida Thales Tozatto Filgueiras Moisés Luiz Lagares Bruno da Silva Macêdo Camila Martins Saporetti Matteo Bodini Leonardo Goliatt |
| author_sort | Cássio Danelon de Almeida |
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| description | The mechanical performance of metallic components is intrinsically linked to their microstructural features. However, the manual quantification of microconstituents in metallographic images remains a time-consuming and subjective task, often requiring over 15 min per image by a trained expert. To address this limitation, this study proposes an automated approach for quantifying the microstructural constituents from low-carbon steel welded zone images using convolutional neural networks (CNNs). A dataset of 210 micrographs was expanded to 720 samples through data augmentation to improve model generalization. Two architectures (AlexNet and VGG16) were trained from scratch, while three pre-trained models (VGG19, InceptionV3, and Xception) were fine-tuned. Among these, VGG19 optimized with stochastic gradient descent (SGD) achieved the best predictive performance, with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.838, MAE of 5.01%, and RMSE of 6.88%. The results confirm the effectiveness of CNNs for reliable and efficient microstructure quantification, offering a significant contribution to computational metallography. |
| format | Article |
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| institution | DOAJ |
| issn | 2079-6439 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e2c6244d9ef1469eae1f4fcad993312b2025-08-20T03:14:31ZengMDPI AGFibers2079-64392025-05-011356610.3390/fib13050066A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded ZonesCássio Danelon de Almeida0Thales Tozatto Filgueiras1Moisés Luiz Lagares2Bruno da Silva Macêdo3Camila Martins Saporetti4Matteo Bodini5Leonardo Goliatt6Computational Modeling Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilDepartment of Electrical Engineering, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilDepartment of Mechanical Engineering, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilDepartment of Computer Science, Federal University of Lavras, Lavras 37200-00, MG, BrazilDepartment of Computational Modeling, Polytechnic Institute, Rio de Janeiro State University, Nova Friburgo 22000-900, RJ, BrazilDipartimento di Economia, Management e Metodi Quantitativi, Università degli Studi di Milano, Via Conservatorio 7, 20122 Milano, ItalyDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, MG, BrazilThe mechanical performance of metallic components is intrinsically linked to their microstructural features. However, the manual quantification of microconstituents in metallographic images remains a time-consuming and subjective task, often requiring over 15 min per image by a trained expert. To address this limitation, this study proposes an automated approach for quantifying the microstructural constituents from low-carbon steel welded zone images using convolutional neural networks (CNNs). A dataset of 210 micrographs was expanded to 720 samples through data augmentation to improve model generalization. Two architectures (AlexNet and VGG16) were trained from scratch, while three pre-trained models (VGG19, InceptionV3, and Xception) were fine-tuned. Among these, VGG19 optimized with stochastic gradient descent (SGD) achieved the best predictive performance, with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> of 0.838, MAE of 5.01%, and RMSE of 6.88%. The results confirm the effectiveness of CNNs for reliable and efficient microstructure quantification, offering a significant contribution to computational metallography.https://www.mdpi.com/2079-6439/13/5/66convolutional neural networksmicrostructure quantificationlow-carbon steeldeep learningimage analysis |
| spellingShingle | Cássio Danelon de Almeida Thales Tozatto Filgueiras Moisés Luiz Lagares Bruno da Silva Macêdo Camila Martins Saporetti Matteo Bodini Leonardo Goliatt A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones Fibers convolutional neural networks microstructure quantification low-carbon steel deep learning image analysis |
| title | A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones |
| title_full | A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones |
| title_fullStr | A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones |
| title_full_unstemmed | A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones |
| title_short | A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones |
| title_sort | cnn based method for quantitative assessment of steel microstructures in welded zones |
| topic | convolutional neural networks microstructure quantification low-carbon steel deep learning image analysis |
| url | https://www.mdpi.com/2079-6439/13/5/66 |
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