Review on Microstructure Analysis of Metals and Alloys Using Image Analysis Techniques

Rekha, S.; Raja, V.K. Bupesh

doi:10.1088/1757-899x/197/1/012010

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…The samples were prepared for metallography analysis as sample cutting→grinding in the rough paper→polishing on emery paper →polishing with diamond paste on polishing sylvet cloth→acid etching with 2% Nital (2% HNO 3 + 98% methanol). 29 The microstructures of samples were studied under a microscope (Metco, MIC-02) with an image analyzer facility.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of optimum condition to improve the microstructure in hot-rolled medium carbon steel by elimination of coarse pearlite patches

Mishra¹,

Kayal²

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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The present study is aimed to eliminate the undesirable microstructure in the real working condition of the steel unit. In this work, the microstructure of hot-rolled medium carbon steel (grade 45C8) is studied for application in the automotive industry. The effect of the temperature and rolling rate on the microstructure has been thoroughly investigated by analyzing the microstructure containing ferrite and pearlite phases after each trial run of hot-rolled steel. The desired microstructure of fine ferrite and pearlite is very much essential in hot-rolled medium carbon steel for better strength and uniform properties of the material. The formation of coarse pearlite is undesirable, which may lead to breakage during drawing and hardness variation in the hot-rolled steel. Therefore, this study is focused to find the optimum condition for achieving the microstructure containing uniform distribution of fine pearlite and ferrite without the presence of blocky pearlite patches.

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Section: Methodsmentioning

confidence: 99%

Evaluation of optimum condition to improve the microstructure in hot-rolled medium carbon steel by elimination of coarse pearlite patches

Mishra¹,

Kayal²

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The material's crystal structure is of great significance to the research and development of modern advanced metal materials [1][2][3]. Micro-domain grains, texture, and others affect the formability of a part, which is directly related to the material's mechanical properties [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction

Shu

Xue

et al. 2022

Crystals

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The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive relationship between the grains in the microstructure, which is a crucial factor affecting the material’s macroscopic properties. Here, we propose a grain knowledge graph representation learning method. First, based on the polycrystalline structure, an advanced digital representation of the knowledge graph is constructed, embedding ingenious knowledge while completely restoring the polycrystalline structure. Then, a heterogeneous grain graph attention model (HGGAT) is proposed to realize the effective high-order feature embedding of the microstructure and to mine the relationship between the structure and the material properties. Through benchmarking with other machine learning methods on magnesium alloy datasets, HGGAT consistently demonstrates superior accuracy on different performance labels. The experiment shows the rationality and validity of the grain knowledge graph representation and the feasibility of this work to predict the material’s structural characteristics.

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“…At present, most tissue structure analysis is based on image analysis technology to extract specific geometric forms and optical density data [12]. However, the data obtained by this method is generally limited to the quantitative information about one-dimensional or two-dimensional images, and it is not easy to directly establish a quantitative relationship between tissue structure and material properties.…”

Section: Introductionmentioning

confidence: 99%

“…The method has obvious limitations. In addition, current material microstructure analysis (e.g., metallographic analysis, XRD analysis, EBSD analysis) is often qualitative or partially quantitative and relies on manual experience [12]. It is still impossible to directly calculate material properties based on the overall organizational structure.…”

Section: Introductionmentioning

confidence: 99%

EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction

Shu¹,

Xin²,

Xie³

2021

Preprint

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The microstructure is an essential part of materials, storing the genes of materials and having a decisive influence on materials' physical and chemical properties. The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance to realize the reverse design of materials, thereby accelerating the research and development of new materials. However, tissue analysis methods of materials science, such as metallographic analysis, XRD analysis, and EBSD analysis, cannot directly establish a complete quantitative relationship between tissue structure and performance. Therefore, this paper proposes a novel dataknowledge-driven organization representation and performance prediction method to obtain a quantitative structure-performance relationship. First, a knowledge graph based on EBSD is constructed to describe the material's mesoscopic microstructure. Then a graph representation learning network based on graph attention is constructed, and the EBSD organizational knowledge graph is input into the network to obtain graphlevel feature embedding. Finally, the graph-level feature embedding is input to a graph feature mapping network to obtain the material's mechanical properties. The experimental results show that our method is superior to traditional machine learning and machine vision methods.

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Review on Microstructure Analysis of Metals and Alloys Using Image Analysis Techniques

Cited by 11 publications

References 14 publications

Evaluation of optimum condition to improve the microstructure in hot-rolled medium carbon steel by elimination of coarse pearlite patches

Evaluation of optimum condition to improve the microstructure in hot-rolled medium carbon steel by elimination of coarse pearlite patches

Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction

EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction

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