Classification of cast iron alloys through convolutional neural networks applied on optical microscopy images
Classification of cast iron alloys based on graphite morphology plays a crucial role in materials science and engineering. Traditionally, this classification has relied on visual analysis, a method that is not only time-consuming but also suffers from subjectivity, leading to inconsistencies. This s...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2024 |
| Country: | España |
| Institution: | Universidad de Cantabria (UC) |
| Repository: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Language: | English |
| OAI Identifier: | oai:repositorio.unican.es:10902/35447 |
| Online Access: | https://hdl.handle.net/10902/35447 |
| Access Level: | Open access |
| Keyword: | Cast iron Convolutional neural networks Deep learning Image classifications Semantic segmentations |
| Summary: | Classification of cast iron alloys based on graphite morphology plays a crucial role in materials science and engineering. Traditionally, this classification has relied on visual analysis, a method that is not only time-consuming but also suffers from subjectivity, leading to inconsistencies. This study introduces a novel approach utilizing convolutional neural networks - MobileNet for image classification and U-Net for semantic segmentation - to automate the classification process of cast iron alloys. A significant challenge in this domain is the limited availability of diverse and comprehensive datasets necessary for training effective machine learning models. This is addressed by generating a synthetic dataset, creating a rich collection of 2400 pure and 1500 mixed images based on the ISO 945-1:2019 standard. This ensures a robust training process, enhancing the model's ability to generalize across various morphologies of graphite particles. The findings showcase a remarkable accuracy in classifying cast iron alloys (achieving an overall accuracy of 98.9±0.4% and exceeding 97% for all six classes - for classification of pure images and ranging between 84% and 93% for semantic segmentation of mixed images) and also demonstrate the model's ability to consistently identify and graphite morphology with a level of precision and speed unattainable through manual methods. |
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