Abstract
With the increasing application of steel materials, the metallographic analysis of steel has gained importance. At present, grain size analysis remains the task of experts who must manually evaluate photos of the structure. Given the software currently available for this task, it is impossible to effectively determine the grain size because of the limitations of traditional algorithms. Artificial intelligence is now being applied in many fields. This paper uses the concept of deep learning to propose a fast image classifier (FIC) to classify grain size. We establish a classification model based on the grain size of steel in metallography. This model boasts high performance, fast operation, and low computational costs. In addition, we use a real metallographic dataset to compare FIC with other deep learning network architectures. The experimental results show that the proposed method yields a classification accuracy of 99.7%, which is higher than existing methods, and boasts computational demands, which are far lower than with other network architectures. We propose a novel system for automatic grain size determination as an application for metallographic analysis.
Highlights
Many industrial processes require information about grain size, a critical metallic microstructure characteristic that significantly influences design parameters such as strength and toughness
We explore and experiment with methods for grain size classification based on deep learning
We propose a fast image classifier (FIC), a novel neural network architecture based on a convolutional neural network (CNN) model
Summary
Many industrial processes require information about grain size, a critical metallic microstructure characteristic that significantly influences design parameters such as strength and toughness. Grain size determination of materials is important in metallic microstructure studies. In the microstructure analysis of metal, traditional methods use image processing to obtain measurements such as grain size and size distribution. Deep learning technology has been widely used to extract features from digital images, resulting in achievements in fields such as image classification, object detection, and image segmentation. We explore and experiment with methods for grain size classification based on deep learning. 3. Compared with the classical deep learning network, the proposed algorithms reduce the number of network layers and weights, decreasing the computing cost while improving the performance of grain size classification over existing methods. Experimental results and comparisons with representative existing methods are discussed in Section 4, and Section 5 concludes and mentions future work
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