Fabrication of P/M In-Situ Fiber Composite Materials Part 1: Formation of Fibrous Structure

Zamzam,; Ross, Denise; Grosch, J.

doi:10.4028/www.scientific.net/kem.79-80.235

Cited by 16 publications

(2 citation statements)

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“…It is also reported by the early works. 11,12 This is attributed to pore nucleation at the B 4 C particulate sites (porosity associated with individual particle) and to the hindered liquid metal flow due to more particle clustering (porosity associated with the particle clusters). and was obtained y120 MPa.…”

Section: Resultsmentioning

confidence: 99%

Hardness and tensile strength study on Al356–B₄C composites

Mazahery

Shabani

Salahi

et al. 2012

Materials Science and Technology

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Additions of high modulus particles to aluminium alloys offer the potential to develop a lightweight composite with high strength and hardness. Al356 matrix composites reinforced with B4C particles were prepared using stir casting method. Hardness and tensile strength of unreinforced alloy and composites with different volume percentages of boron carbide particles were measured and compared. Microstructural characterisation revealed an almost uniform distribution of the B4C particles in the matrix. It also showed that the grain size of aluminium composite is smaller than that of monolithic aluminium. X-ray diffraction studies also confirmed the existence of boron carbide and some other reaction products, such as AlB2 and Al3BC, in composite samples. It is observed that the mechanical properties enhance with increasing particle fraction. The great enhancement in values of ultimate tensile stress and hardness observed in this experiment is due to small particle size and good distribution of the B4C particles, which was confirmed by SEM images. According to the results of this experiment, quite significant improvement in strength is noted when 10 vol.-B4C particles is added; a further increase in B4C content leads to a reduction in strength values. This might be the result of greater agglomeration of particles and higher degree of microporosity present in the composite at higher B4C content.

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Section: Resultsmentioning

confidence: 99%

Hardness and tensile strength study on Al356–B₄C composites

Mazahery

Shabani

Salahi

et al. 2012

Materials Science and Technology

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show abstract

“…The effect of the size and percentage by weight of the reinforcements has been considered in the wear study. Powder metallurgy [6] , stir casting [7] , pressure infiltration [8] , squeeze casting [9] are the techniques used in the manufacture of MMCs. Stircasting is the economical way of getting good distribution of reinforcements.…”

Section: Introductionmentioning

confidence: 99%

ANN Based Prediction of Effect of Reinforcements on Abrasive Wear Loss and Hardness in a Hybrid Metal Matrix Composites

Selladurai¹

2009

American J. of Engineering and Applied Sciences

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Problem statement:The reinforcements added to an alloy lead to variation in properties. The content and size of the reinforcement influences the properties of composites. Very little research has been carried out in hybrid composites. Work on hybrid LM6 aluminium alloy metal matrix composites (MMC) with flyash and SiC has been initiated here. The effect of the four parameters, size and weight of the reinforcements on the hardness and wear loss has been studied. Approach: Artificial neural networks, from the artificial intelligence family, is a type of information processing system, based on modeling the neural system of human brain. The effect of the parameters was investigated using ANN. Central composite rotatable method of design of experiments was used to arrive at the combination and the number of specimens. The specimens were prepared using the liquid metallurgy route and tested. Pin-on-disc apparatus was used for determining wear. Rockwell hardness on C scale was determined. The data from the experiments were used for training and testing the network. Results: The accuracy in ANN prediction was appreciable with the error estimated for wear loss and hardness being less than 2%. Conclusions/Recommendations: The ANN prediction is quick and economical way of estimating the properties.

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Application of Finite Element Model and Artificial Neural Network in Characterization of Al Matrix Nanocomposites Using Various Training Algorithms

Shabani

Mazahery

2012

Metall Mater Trans A

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Fabrication of P/M In-Situ Fiber Composite Materials Part 1: Formation of Fibrous Structure

Cited by 16 publications

References 0 publications

Hardness and tensile strength study on Al356–B₄C composites

Hardness and tensile strength study on Al356–B₄C composites

ANN Based Prediction of Effect of Reinforcements on Abrasive Wear Loss and Hardness in a Hybrid Metal Matrix Composites

Application of Finite Element Model and Artificial Neural Network in Characterization of Al Matrix Nanocomposites Using Various Training Algorithms

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Fabrication of P/M In-Situ Fiber Composite Materials Part 1: Formation of Fibrous Structure

Cited by 16 publications

References 0 publications

Hardness and tensile strength study on Al356–B4C composites

Hardness and tensile strength study on Al356–B4C composites

ANN Based Prediction of Effect of Reinforcements on Abrasive Wear Loss and Hardness in a Hybrid Metal Matrix Composites

Application of Finite Element Model and Artificial Neural Network in Characterization of Al Matrix Nanocomposites Using Various Training Algorithms

Contact Info

Product

Resources

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Hardness and tensile strength study on Al356–B₄C composites

Hardness and tensile strength study on Al356–B₄C composites