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Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN
by
Stojanović, Blaža
, Gajević, Sandra
, Güler, Onur
, Özkaya, Serdar
, Miladinović, Slavica
in
Aluminum base alloys
/ Aluminum boron carbide
/ Aluminum composites
/ Aluminum matrix composites
/ Artificial neural networks
/ Automotive materials
/ Back propagation networks
/ Ball milling
/ Coefficient of friction
/ Composite fabrication
/ Composite materials
/ Ductility
/ Frictional wear
/ Grain boundaries
/ Hot extrusion
/ Interfacial bonding
/ Mechanical properties
/ Metal matrix composites
/ Nanocomposites
/ Optimization
/ Parameters
/ Particle size
/ Particulate composites
/ Powder metallurgy
/ Predictions
/ Product development
/ Sliding friction
/ Taguchi methods
/ Tensile strength
/ Variance analysis
/ Yield stress
2024
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Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN
by
Stojanović, Blaža
, Gajević, Sandra
, Güler, Onur
, Özkaya, Serdar
, Miladinović, Slavica
in
Aluminum base alloys
/ Aluminum boron carbide
/ Aluminum composites
/ Aluminum matrix composites
/ Artificial neural networks
/ Automotive materials
/ Back propagation networks
/ Ball milling
/ Coefficient of friction
/ Composite fabrication
/ Composite materials
/ Ductility
/ Frictional wear
/ Grain boundaries
/ Hot extrusion
/ Interfacial bonding
/ Mechanical properties
/ Metal matrix composites
/ Nanocomposites
/ Optimization
/ Parameters
/ Particle size
/ Particulate composites
/ Powder metallurgy
/ Predictions
/ Product development
/ Sliding friction
/ Taguchi methods
/ Tensile strength
/ Variance analysis
/ Yield stress
2024
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Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN
by
Stojanović, Blaža
, Gajević, Sandra
, Güler, Onur
, Özkaya, Serdar
, Miladinović, Slavica
in
Aluminum base alloys
/ Aluminum boron carbide
/ Aluminum composites
/ Aluminum matrix composites
/ Artificial neural networks
/ Automotive materials
/ Back propagation networks
/ Ball milling
/ Coefficient of friction
/ Composite fabrication
/ Composite materials
/ Ductility
/ Frictional wear
/ Grain boundaries
/ Hot extrusion
/ Interfacial bonding
/ Mechanical properties
/ Metal matrix composites
/ Nanocomposites
/ Optimization
/ Parameters
/ Particle size
/ Particulate composites
/ Powder metallurgy
/ Predictions
/ Product development
/ Sliding friction
/ Taguchi methods
/ Tensile strength
/ Variance analysis
/ Yield stress
2024
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Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN
Journal Article
Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN
2024
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Overview
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.
Publisher
MDPI AG,MDPI
Subject
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