Prediction of effect of fabrication parameters on the properties of B4C ceramic particle reinforced AA2024 matrix nanocomposites using neural networks

Varol, Temel; Çanakçı, Aykut; Özşahin, Şükrü; Beder, Murat; Akçay, Serhatcan Berk

doi:10.1016/j.mtcomm.2024.109279

Materials Today Communications

2024

DOI: 10.1016/j.mtcomm.2024.109279

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Prediction of effect of fabrication parameters on the properties of B4C ceramic particle reinforced AA2024 matrix nanocomposites using neural networks

Temel Varol,

Aykut Çanakçı,

Şükrü Özşahin

et al.

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2024

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Cited by 2 publications

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Influence of Polyethylene Glycol and Methanol Additions on the Mechanical Alloying Behavior of Cu-4B4C Composite Powder

BIYIK

2024

IJCESEN

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This study investigated the effect of different process control agent (PCA) usage on mechanical alloying behavior of boron carbide (B4C) reinforced copper (Cu) based composite powder. For this purpose, elemental Cu and B4C powders were weighed and powder specimens were prepared with respect to appropriate mass ratios (96% Cu and 4% B4C). Two different PCA additives, namely polyethylene glycol (PEG) and methanol, were also used to prepare powder samples. The amount of PCA was kept constant at 5wt.% for both specimen. These prepared powder samples were then milled using a planetary type ball-mill. After specified milling periods, milling runs were interrupted and powder samples were extracted from the milling vials for further powder characterization including powder morphology using scanning electron microscopy (SEM) and average particle size (APS) via laser diffraction analysis (Mastersizer). Accordingly, after the completion of milling runs, namely 15 hours of ball-milling, methanol addition was found much more effective at reducing particle sizes than PEG. Final APS values for powder specimens having PEG and methanol as PCAs were determined to be 8.237 and 4.101 microns, respectively.

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Influence of Polyethylene Glycol and Methanol Additions on the Mechanical Alloying Behavior of Cu-4B4C Composite Powder

BIYIK

2024

IJCESEN

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Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN

Gajević,

Miladinović,

Güler

et al. 2024

Materials

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The presented study investigates the effects of weight percentages of boron carbide reinforcement on the wear properties of aluminum alloy composites. Composites were fabricated via ball milling and the hot extrusion process. During the fabrication of composites, B4C content was varied (0, 5, and 10 wt.%), as well as milling time (0, 10, and 20 h). Microstructural observations with SEM microscopy showed that with an increase in milling time, the distribution of B4C particles is more homogeneous without agglomerates, and that an increase in wt.% of B4C results in a more uniform distribution with distinct grain boundaries. Taguchi and ANOVA analyses are applied in order to investigate how parameters like particle content of B4C, normal load, and milling time affect the wear properties of AA2024-based composites. The ANOVA results showed that the most influential parameters on wear loss and coefficient of friction were the content of B4C with 51.35% and the normal load with 45.54%, respectively. An artificial neural network was applied for the prediction of wear loss and the coefficient of friction. Two separate networks were developed, both having an architecture of 3-10-1 and a tansig activation function. By comparing the predicted values with the experimental data, it was demonstrated that the well-trained feed-forward-back propagation ANN model is a powerful tool for predicting the wear behavior of Al2024-B4C composites. The developed models can be used for predicting the properties of Al2024-B4C composite powders produced with different reinforcement ratios and milling times.

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Prediction of effect of fabrication parameters on the properties of B4C ceramic particle reinforced AA2024 matrix nanocomposites using neural networks

Cited by 2 publications

References 61 publications

Influence of Polyethylene Glycol and Methanol Additions on the Mechanical Alloying Behavior of Cu-4B4C Composite Powder

Influence of Polyethylene Glycol and Methanol Additions on the Mechanical Alloying Behavior of Cu-4B4C Composite Powder

Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN

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