Artificial neural network technique to predict dynamic fracture of particulate composite

Kushvaha, Vinod; Kumar, S. Anand; Madhushri, Priyanka

doi:10.1177/0021998320911418

Cited by 54 publications

(16 citation statements)

References 24 publications

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“…Error back propagation (BP) algorithm and support vector machine (SVM) are two commonly used machine learning algorithms. They have many applications in material studies [35,36]; thus, they are applied in ML. The BP algorithm is composed of two processes: forward calculation of data stream (forward propagation) and backward propagation of error signal.…”

Section: MLmentioning

confidence: 99%

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide

et al. 2021

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Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from about 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron–carbon ratio and provide a new design solution for other multi-element systems.

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Section: MLmentioning

confidence: 99%

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide

et al. 2021

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“…Arti cial Neural Network (ANN) is a learning algorithm that implicitly describes the nonlinear and complex relationship between input data and output results [39,56]. In the present study, the commercially available STATISTICA, version 12 software was used.…”

Section: Ann Proceduresmentioning

confidence: 99%

Exploring Efficiency of Biochar in Enhancing Water Retention in Soils with Varying Grain Size Distributions using ANN Technique

Garg

Wani

Zhu

et al. 2021

Preprint

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Recently, incentives have been provided in many countries, including Canada and Denmark, to produce biochar for construction usage. This is done because biochar is carbon negative and can help achieve the emission reduction goal of 2030. This technical note aims to analyze the efficiency of biochar in soils with varying grain size distribution for enhancing water retention capacity (WRC). The combinations of biochar content and grain size distributions corresponding to the maximum and minimum efficiency were explored. Artificial Neural Network (ANN) based model for predicting Soil Water Characteristic Curve (SWCC) as a function of soil suction and grain size distribution was developed. A new factor (the ratio of fine (silt + clay) and coarse (sand) content) was proposed for the interpretation of the efficiency of biochar in soils. The newly developed model is able to predict SWCC reasonably well. Biochar amendment is found to influence both dry and wet sides of soils with a clay content lower than threshold content (6–8%). Beyond threshold content, the influence of biochar appears to reduce. However, in the case of high sand content soils (90%), the NWC value on the drier side is generally higher as compared to soils with lower sand content. Based on sensitivity analysis, it was found that the ratio of fine to sand content is the most influential, while biochar content is the least influential.

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“…Error back propagation algorithm (BP) and support vector machine (SVM) are two commonly used machine learning algorithms. They have many applications in materials research [32,33]; thus, they are applied in ML. The BP algorithm is and threshold values θ , ;…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Machine Learning and a Computational Fluid Dynamic Approach to Estimate Phase Composition of Chemical Vapor Deposition Boron Carbide

Zeng

Gao

Guan

et al. 2020

Preprint

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Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron-carbon ratio and provide a new design solution for other multi-element systems.

show abstract

Artificial neural network technique to predict dynamic fracture of particulate composite

Cited by 54 publications

References 24 publications

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide

Exploring Efficiency of Biochar in Enhancing Water Retention in Soils with Varying Grain Size Distributions using ANN Technique

Machine Learning and a Computational Fluid Dynamic Approach to Estimate Phase Composition of Chemical Vapor Deposition Boron Carbide

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