Feature Selection and Neural Network for analysis of microstructural changes in magnetic materials

Benítez, José Alberto Pérez; Padovese, Linilson Rodrigues

doi:10.1016/j.eswa.2011.02.088

Cited by 25 publications

(15 citation statements)

References 19 publications

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“…Machine learning has been utilized in various areas of materials science and engineering to develop constitutive relations that establish structure property relationships [48][49][50][51][52][53][54]. Many such studies treat the resultant machine learning models as a purely black box approach, used to predict an event without consideration to interpreting the decision framework behind the prediction.…”

Section: Machine Learningmentioning

confidence: 99%

Insights into twinning in Mg AZ31: A combined EBSD and machine learning study

Orme

Chelladurai

Rampton

et al. 2016

Computational Materials Science

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To explore the driving forces behind deformation twinning in Mg AZ31, a machine learning framework is utilized to mine data obtained from electron backscatter diffraction (EBSD) scans in order to extract correlations in physical characteristics that cause twinning. The results are intended to inform physics-based models of twin nucleation and growth. A decision tree learning environment is selected to capture the relationships between microstructure and twin formation; this type of model effectively highlights the more influential characteristics of the local microstructure. Trees are assembled to analyze both twin nucleation in a given grain, and twin propagation across grain boundaries. Each model reveals a unique combination of crystallographic attributes that affect twinning in the Mg. Twin nucleation is found to be mostly controlled by a combination of grain size, basal Schmid factor, and bulk dislocation density while twin propagation is affected most by grain boundary length, basal Schmid factor, angle from grain boundary plane to the RD plane, and grain boundary misorientation. The machine

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Section: Machine Learningmentioning

confidence: 99%

Insights into twinning in Mg AZ31: A combined EBSD and machine learning study

Orme

Chelladurai

Rampton

et al. 2016

Computational Materials Science

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“…61 The process of SFS is shown in Figure 4a Step (d) is GA feature selection. Unlike SFS, which is a greedy algorithm that tends to fall into the problem of local optimality, 62 GA is one of the most efficient feature selection methods. It simulates the biological evolution mechanism of "survival of the fittest" by iteratively updating the individuals in the population to find the global optimal result without trying all the feature combinations.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

Ma,

Li,

et al. 2023

J. Chem. Inf. Model.

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“…However, the regression models (e.g. linear regression [7] , neural network [9][10][11] , and partial linear regression method [3][4] ) adopted in the previous mentioned methods share the limited capacity. Among different regression tools, the polynomial-based multi-variable regression outperforms other methods in regression accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Hardness of X12m Using Barkhausen Noise and Chebyshev Polynomials Regression Methods

Li²,

Wang³

et al. 2020

Studies in Applied Electromagnetics and Mechanics

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Barkhausen noise (BN) is electromagnetic pulse sequence that could be used to nondestructively predict the properties of materials such as hardness, residual stress and carbon content. Current BN signal analysis methods fail to describe the highly variated BN signal and achieve high regression accuracy due to the low interpretability of neural network and limited capacity of mathematical regression tools. In this paper, two multi-variable regression tools, named partial Chebyshev polynomial regression (PCPR) and Mutual Information-based Feature Selection with Class-dependent Redundancy and multi-variable Chebyshev polynomials regression (MIFS-CR+MCPR), are employed for the first time to predict the hardness of Cr12MoV steel (i.e. X12m). Combined with Chebyshev polynomials, our regression tools are designed on the basis of cascaded regression and mutual-information-based feature selection. As represented by the experimental results for predicting the hardness of X12m, the proposed method outperforms other comparative methods including neural network and partial linear square regression method.

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Feature Selection and Neural Network for analysis of microstructural changes in magnetic materials

Cited by 25 publications

References 19 publications

Insights into twinning in Mg AZ31: A combined EBSD and machine learning study

Insights into twinning in Mg AZ31: A combined EBSD and machine learning study

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

Prediction of the Hardness of X12m Using Barkhausen Noise and Chebyshev Polynomials Regression Methods

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