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Article
Computer Science, Artificial Intelligence
Julian Luengo et al.
Summary: This paper reviews and categorizes computer vision techniques for metallographic image segmentation, introduces deep learning-based ensemble techniques utilizing pixel similarity, and conducts thorough comparisons in real-world datasets to discuss strengths, weaknesses, and application frameworks. The paper also addresses open challenges in the field to provide guidance for future research to fill existing gaps.
INFORMATION FUSION
(2022)
Article
Materials Science, Multidisciplinary
T. Martinez Ostormujof et al.
Summary: This study explores the application of Convolutional Neural Networks in extracting features from EBSD data for automatic phase classification, using ferrite-martensite discrimination as a case study. The results show that utilizing orientation-derived parameters can improve classification accuracy, and the proposed models achieve higher accuracies in shorter times compared to other methods.
MATERIALS CHARACTERIZATION
(2022)
Article
Materials Science, Multidisciplinary
S. Breumier et al.
Summary: In this study, a U-Net model was trained to segment bainite, ferrite, and martensite on EBSD maps using the kernel average misorientation and the pattern quality index as input. The introduction of an unknown class during training simplified the manual labeling process. The investigation of providing maps with different acquisition steps, indexation quality, and constituent content to the model highlighted the significance of training the model with a diverse range of configurations. The model achieved a 92% mean accuracy in differentiating the three constituents. The inclusion of an additional channel containing the map acquisition step aided the model in generalizing to various EBSD acquisition steps.
MATERIALS CHARACTERIZATION
(2022)
Article
Materials Science, Multidisciplinary
Marc Ackermann et al.
Summary: This study utilized deep learning to quantify the martensite-austenite (M-A) islands in bainite. The research found that the ratio of constituent to image size during data pre-processing is a crucial parameter affecting the accuracy of subsequently trained models.
MATERIALS CHARACTERIZATION
(2022)
Article
Materials Science, Multidisciplinary
Boguslawa Adamczyk-Cieslak et al.
Summary: This study developed a carbide-free bainitic steel based on low-alloy steel, which exhibited superior mechanical properties after rolling and cooling, thanks to its fine carbide-free bainitic microstructure.
METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE
(2021)
Article
Materials Science, Multidisciplinary
Shuai Wang et al.
Summary: This paper proposes a method combining improved ResNet50 and enhanced faster R-CNN to reduce the average running time and improve accuracy in automatic detection of steel surface defects. By analyzing the dataset obtained in a real factory environment, the accuracy of this method can reach 98.2%, with faster running time compared to other models.
Article
Materials Science, Multidisciplinary
Wei Tian et al.
Summary: The study proposes a new method for segmenting and extracting concrete CT scanning images based on regional convolution neural network (Mask R-CNN), achieving high-precision recognition and segmentation of voids and cracks. Through 3D reconstruction and observation under real-time loading, the propagation of meso-cracks can be investigated.
MATERIALS & DESIGN
(2021)
Article
Materials Science, Multidisciplinary
Jiwon Yeom et al.
Summary: Researchers successfully segmented experimental materials science data using a SegNet-based CNN trained only with simple phase field simulations. To enhance the CNN's understanding of image content, they manipulated the training images in various ways. This approach of training CNNs with computationally generated images accelerates the pace of materials design and discovery.
Article
Materials Science, Multidisciplinary
Dierk Raabe et al.
METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE
(2020)
Article
Multidisciplinary Sciences
Hoheok Kim et al.
SCIENTIFIC REPORTS
(2020)
Review
Engineering, Civil
Lukasz Madej
ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING
(2017)
Article
Engineering, Multidisciplinary
Siddhartha Banerjee et al.
Article
Computer Science, Artificial Intelligence
Dan Ciresan et al.
Article
Materials Science, Characterization & Testing
Victor Hugo C. de Albuquerque et al.
NONDESTRUCTIVE TESTING AND EVALUATION
(2008)
