# Advanced Steel Microstructural Classification by Deep Learning Methods

arXiv: https://arxiv.org/abs/1706.06480 · HTML: https://mlsanity.com/paper/1706.06480/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 16 of 16)

- [Addressing materials' microstructure diversity using transfer learning](https://mlsanity.com/paper/2107.13841/) (2021)
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- [Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives](https://mlsanity.com/paper/2208.04149/) (2022)
- [A Deep Learning Approach for Pixel-level Material Classification via Hyperspectral Imaging](https://mlsanity.com/paper/2409.13498/) (2024)
- [Digital Fingerprinting of Microstructures](https://mlsanity.com/paper/2203.13718/) (2022)
- [Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing](https://mlsanity.com/paper/2302.08495/) (2023)
- [Context Determines Optimal Architecture in Materials Segmentation](https://mlsanity.com/paper/2602.04154/) (2026)
- [Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy](https://mlsanity.com/paper/2305.07790/) (2023)
- [Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation](https://mlsanity.com/paper/2309.00058/) (2023)
- [Macroscale fracture surface segmentation via semi-supervised learning considering the structural similarity](https://mlsanity.com/paper/2403.18337/) (2024)
- [AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth](https://mlsanity.com/paper/2404.08017/) (2024)
- [A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images](https://mlsanity.com/paper/2502.07107/) (2025)
- [Physics Informed Generative AI Enabling Labour Free Segmentation For Microscopy Analysis](https://mlsanity.com/paper/2602.01710/) (2026)
- [Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning](https://mlsanity.com/paper/2607.28695/) (2026)
- [Stride Independent Patching for Deep Learning](https://mlsanity.com/paper/2610.12216/) (2026)

## Abstract (arXiv)

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.