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Neural Computing and Applications

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24.06.2017 | Original Article

Evolution of the size distribution of Al–B₄C nano-composite powders during mechanical milling: a comparison of experimental results with artificial neural networks and multiple linear regression models

verfasst von: F. Akhlaghi, M. Khakbiz, A. Rezaii Bazazz

Erschienen in: Neural Computing and Applications | Sonderheft 2/2019

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Abstract

In the present study, two three-layer feed-forward artificial neural networks (ANNs) and multiple linear regression (MLR) models were developed for modeling the effects of material and process parameters on the powder particle size characteristics generated during high-energy ball milling of Al and B₄C powders. The investigated process parameters included aluminum particle size, B₄C size and its content as well as milling time. The median particle size (D₅₀) and the extent of size distribution (D₉₀–D₁₀) were considered as target values for modeling. The developed ANN and MLR models could reasonably predict the experimentally determined characteristics of powders during mechanical milling.

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Titel: Evolution of the size distribution of Al–B4C nano-composite powders during mechanical milling: a comparison of experimental results with artificial neural networks and multiple linear regression models
verfasst von: F. Akhlaghi
M. Khakbiz
A. Rezaii Bazazz
Publikationsdatum: 24.06.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3082-9

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