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RESEARCH PAPER · Metals · 2024

Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures

Martin Johannes Müller, Marie Stiefel, Björn-Ivo Bachmann, Dominik Britz, Frank Mücklich

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Overview: Machine Learning for Segmentation and Classification of Complex Steel MicrostructuresOriginalAutomatic Microstructural Classification of Ultrahigh Carbon Steel with Vision Transformers and Convolutional Neural Networks4Efficient Phase Segmentation of Light-Optical Microscopy Images of Highly Complex Microstructures Using a Correlative Approach in Combination with Deep Learning Techniques6Prediction of Composite Performance of Refractory and Ceramic Materials Based on Deep Learning3Enhanced Three-Stage Cluster-Then-Classify Method (ETSCCM)1The Role of Artificial Intelligence in Microstructural Characterization of Ferrous Materials: A Review of Tools, Findings, and Research Challenges1Modeling the spread of forest fires through cellular automata by leveraging deep learning to derive transition rules12Quantitative Microstructure of Multiphase Al-Zn-Si-(Mg) Coatings and Their Effects on Sacrificial Protection for Steel6Material Characterization of Aluminum Castings Using Machine Learning Techniques4Microstructure classification and the polycrystalline microstructure state space2Multi-task learning for joint classification and segmentation of metallographic microstructures4Digital image processing and machine learning for automated microstructural characterization and grain size measurement in ferritic-pearlitic steels and nodular cast irons2Hybrid machine learning and regression framework for automated phase classification and quantification in SEM images of commercial steels4Elman Network Classifier Based on Hyperactivity Rat Swarm Optimizer and Its Applications for AlSi10Mg Process Classification0Machine learning to predict phase transformation products and their morphologies – Application in design of lean high strength steel10Automatic and Rapid Measurement in Artificial Intelligence-Aided Microstructure Analysis: A Deep Learning Approach Applied to AlSi9 Alloys0Laser-material interaction analysis of TiO2: optical, structural, and chemical insights0TD U-Net for Shell Segmentation and Thickness Evaluation in Core–Shell TiO2 TEM Images3Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods0Multi-carbides segmentation and quantitative morphological parameters extraction with machine vision0Freeze-thaw-induced microstructural damage in polyester fiber-reinforced cementitious composites revealed by X-ray microtomography2Pixels to properties0Deep learning-driven innovation in metallic materials: A comprehensive review on microstructure analysis, property prediction, and inverse design14A Dynamic‐Weighted Deep Transfer Learning Framework for Thermal Conductivity Prediction and Analysis2Machine learning for materials microstructures: A survey of applications and methodologies5A baseline study of rare-phase detection in ultra-high carbon steel microstructures using deep learning and handcrafted texture features0Anisotropic hydrogen embrittlement behavior of hot-rolled A572 steel0Advancing AI-Driven Microstructure Analysis Through Correlative Microscopy Approaches0Deep-learning-based grain boundary segmentation and quantitative microstructural characterization of normalized 1045 steel from optical microscopy0From sample value to metallurgical roles: Constructing compact core sets for steel mechanical property interpretability modelling0Recent progress in machine learning strategies in designing, synthesizing and characterizing new age metal matrix composites0Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions0Aligning Expert Knowledge With Microstructural Features: A Cross‐Modal CLIP Framework for Continuous Spheroidization Grading of 12Cr1MoV Steel0Microstructure-Informed Prediction of Transformation Products in C-Mn and C-Mn-Nb Steels for Data-Driven Process-Window Design0Machine learning based microstructure classification using SEM images and morphological features0A critical review: Machine learning and advanced simulation in steel heat treatment0

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  3. Prediction of Composite Performance of Refractory and Ceramic Materials Based on Deep Learning

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  7. Quantitative Microstructure of Multiphase Al-Zn-Si-(Mg) Coatings and Their Effects on Sacrificial Protection for Steel

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  9. Microstructure classification and the polycrystalline microstructure state space

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  10. Multi-task learning for joint classification and segmentation of metallographic microstructures

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  11. Digital image processing and machine learning for automated microstructural characterization and grain size measurement in ferritic-pearlitic steels and nodular cast irons

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  13. Elman Network Classifier Based on Hyperactivity Rat Swarm Optimizer and Its Applications for AlSi10Mg Process Classification

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  15. Automatic and Rapid Measurement in Artificial Intelligence-Aided Microstructure Analysis: A Deep Learning Approach Applied to AlSi9 Alloys

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  16. Laser-material interaction analysis of TiO2: optical, structural, and chemical insights

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  17. TD U-Net for Shell Segmentation and Thickness Evaluation in Core–Shell TiO2 TEM Images

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  18. Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods

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  19. Multi-carbides segmentation and quantitative morphological parameters extraction with machine vision

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  20. Freeze-thaw-induced microstructural damage in polyester fiber-reinforced cementitious composites revealed by X-ray microtomography

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  21. Pixels to properties

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  26. Anisotropic hydrogen embrittlement behavior of hot-rolled A572 steel

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