Analysis of the effect of reinforcement particles on the compressibility of Al–SiC composite powders using a neural network model

Hafizpour, H.R.; Sanjari, Mehdi; Simchi, A.

doi:10.1016/j.matdes.2008.07.052

Cited by 58 publications

(41 citation statements)

References 30 publications

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“…The artificial neuron output value, which depends on the selected activation function employs a sigmoid function as the activation function [18,19,23] and is calculated using Eq. (2).…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…On the other hand, reinforcement clustering and the need for extra deformation of the soft particles to fill the gaps between the contact points of hard particles decrease compressibility. This situation is particularly effective when long-range networks of reinforcement exist [18]. It is also feasible that agglomeration regions by formed coarse or fine B 4 C particles restrict the movement of Al-Cu-Mg alloy particles, preventing the rearrangement of densification.…”

Section: Effects Of the Reinforcement Size And Ratiomentioning

confidence: 99%

“…ANN is a well established field over the last 20 years because it can often solve problems much faster compared with other approaches with the additional ability to learn from a small set of experimental data [1,2]. The use of ANNs represents a new methodology in many different applications of the composite materials including prediction of physical and mechanical properties [17][18][19]. However, an artificial neural network (ANN) model has not yet been established for understanding the effects of reinforcement size, reinforcement ratio, compact pressure, and milling time on the density and porosity of Al2024-B 4 C MMCs produced by powder metallurgy.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B4C Composites Using Artificial Neural Networks

Varol

Çanakçı

Özşahin

2014

Acta Metall. Sin. (Engl. Lett.)

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Al-Cu-Mg/B 4 Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B 4 C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al-Cu-Mg/B 4 Cp composites. This model can be used for predicting the densification behavior of Al-Cu-Mg/B 4 Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%.

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“…The artificial neuron output value, which depends on the selected activation function employs a sigmoid function as the activation function [18,19,23] and is calculated using Eq. (2).…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Section: Effects Of the Reinforcement Size And Ratiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B4C Composites Using Artificial Neural Networks

Varol

Çanakçı

Özşahin

2014

Acta Metall. Sin. (Engl. Lett.)

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“…There is no undesirable reaction and segregation between the components during producing with this method 8 . Al-SiC composite powder can be sintered by several methods such as conventional pressure less sintering, hot pressing, hot extrusion, spark plasma sintering and etc [9][10][11][12][13][14] . Shaping method has very significant effect on physical and mechanical properties of this composite.…”

Section: Inroductionmentioning

confidence: 99%

Effect of shaping methods on the mechanical properties of Al-SiC composite

Zakeri

Rudi

2013

Mat. Res.

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Al-SiC composites were successfully produced with improved mechanical properties. Effect of SiC content, its particles size and shaping method were investigated. Three shaping methods of conventional powder metallurgy (PM), hot press (HP) and hot extrusion (EX) were used. The hardness of the samples was measured by Vickers method. Tensile and compression tests were performed for the characterization of mechanical properties. Microstructure was monitored by optical microscopy. Maximum relative density (RD) was obtained for hot pressed samples. Higher SiC content with smaller particles size had the best effect on the mechanical properties such as yielding point (YP), ultimate tensile strength (UTS) and hardness. Maximum hardness (6.57 GPa) and UTS (212 MPa) were obtained for Al-20%SiC with SiC mesh size of 1000. Maximum YP was obtained for that is larger than it in tensile test (117.96 MPa). Remarkable difference exists between HP and extrusion methods at higher SiC contents. The YP of EX samples are larger than HP samples. Strains at known stress (562 MPa) of EX samples are smaller than HP samples.

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“…For instance, [3] investigated analysis of the effect of reinforcement particles on compressibility of AI -SIC composite powders, [4] studied prediction of corrosion rate of magnesium-rare earth alloys, and [5] investigated prediction of corrosion behavior of alloy 22 using neural network as a data mining tool. Artificial neural network corrosion modeling for metals in an equatorial climate was studied by [6].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Prediction of Viscosity Index and Pour Point of Some Bio Lubricants from Selected Oil Plants

Onogu¹,

Oseni²,

Ashwe³

2016

IJAIS

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Analysis of the effect of reinforcement particles on the compressibility of Al–SiC composite powders using a neural network model

Cited by 58 publications

References 30 publications

Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B4C Composites Using Artificial Neural Networks

Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B4C Composites Using Artificial Neural Networks

Effect of shaping methods on the mechanical properties of Al-SiC composite

Artificial Neural Network Prediction of Viscosity Index and Pour Point of Some Bio Lubricants from Selected Oil Plants

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