Microstructural characteristics and tensile properties of nano-composite Al 2014/4wt.% Al2O3 produced from machining chips

Roshan, M.R.; Mirzaei, Maryam; Jahromi, S.A. Jenabali

doi:10.1016/j.jallcom.2013.03.182

Cited by 24 publications

(6 citation statements)

References 23 publications

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“…In addition, recycling processes based on other SPD methods such as cyclic extrusion compression (CEC) [16], equal channel angular pressing (ECAP) [17][18][19], high-pressure torsion (HPT) [20] or compressive torsion [21], and combined use of forward extrusion and ECAP [22,23] have also been reported. Recently, another study [24] aimed at producing a composite material including nano-particles from machining chips via hot extrusion, and a new method was proposed to produce clad plates from iron chips by using hot rolling [25]. Almost all of the abovementioned methods require precompaction of the machining swarf, the technical details of which have been reported elsewhere [26] literature published before 2010 can be found in our previous paper [27].…”

Section: Introductionmentioning

confidence: 99%

Solid-state recycling of aluminium alloy swarf into c-channel by hot extrusion

Chiba

Yoshimura

2015

Journal of Manufacturing Processes

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Section: Introductionmentioning

confidence: 99%

Solid-state recycling of aluminium alloy swarf into c-channel by hot extrusion

Chiba

Yoshimura

2015

Journal of Manufacturing Processes

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“…Application of a modified Williamson-Hall method, after calculation of the values D and ε, can be used for determine dislocation density (ρ d ) of the observed material by the equation [15]:…”

Section: Methodsmentioning

confidence: 99%

Prediction of hardness and electrical properties in ZrB2 particle reinforced metal matrix composites using artificial neural network

Ružić¹,

Antanasijević²,

Božić³

et al. 2014

Metall Mater Eng

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In the present study, the hardness and electrical properties of copper based composite prepared by hot pressing of mechanically alloyed powders were predicted using Artificial Neural Network (ANN) approach. Milling time (t, h), particles size of mechanically alloyed powders (d, nm), dislocation density (ρ, m -2 ) and compressive yield stress (σ 0.2 , MPa) were used as inputs. The ANN model was developed using general regression neural network (GRNN) architecture. Cu-based composites reinforced with micro and nano ZrB 2 particles were consolidated via powder metallurgy processing by combining mechanical alloying and hot pressing. Analysis of the obtained results concerning hardness and electrical properties of the Cu-7 vol.% ZrB 2 alloy showed that the distribution of micro and nano ZrB 2 particles and the presence of agglomerates in the Cu matrix directly depend on the milling time. Also, the results show a strong influence of the milling time on hardness and electrical properties of Cu-7 vol.% ZrB 2 alloy. Addition of ZrB 2 particles decreases electrical conductivity of copper, but despite this fact Cu-7 vol.% ZrB 2 alloy can be marked as highly conductive alloy (samples made of mechanically alloyed powders milled longer than 20 h). Experimental results of the samples have shown a consistency with the predicted results of ANN.

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“…As a result, 3 vol.% CNT/Al composites exhibited good mechanical properties, with tensile strength enhanced by 65.7% and modulus increased by 28.9% as compared to the unreinforced Al matrix, while keeping a fine ductility of 8.8%. Roshan et al [ 11 ] studied the microstructure and tensile properties of nanocomposite AA2014–4 wt.% Al 2 O 3 produced from machining chips using PM. Their results showed that Al 2 O 3 nanoparticles were very well-introduced and dispersed in the AA2014 matrix after the milling, but they reported an intergranular fracture of extruded particles that was accelerated by weak grain boundaries (GBs).…”

Section: Introductionmentioning

confidence: 99%

Investigation of Microstructures and Mechanical Properties of SiC/AA2024 Nanocomposites Processed by Powder Metallurgy and T6 Heat Treatment

Zhang

Liang

et al. 2022

Materials

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SiC/AA2024 nanocomposites with 1 and 5 vol.% SiC nanoparticles have been prepared by a powder metallurgy route involving high-energy ball-milling (HEBM), spark plasma sintering (SPS), and hot extrusion. The microstructures and mechanical properties of the nanocomposite samples before and after T6 heat treatment were investigated. The samples exhibited a bimodal microstructure with SiC nanoparticles being dispersed in it. With increasing the SiC nanoparticle content from 1 to 5 vol.%, the yield strength (YS) and ultimate tensile strength (UTS) increased and the elongation to fracture (El) slightly decreased. After T6 heat treatment, a simultaneous improvement of the strength and ductility was observed, with the YS, UTS, and El increasing from 413 MPa, 501 MPa, and 5.4% to 496 MPa, 572 MPa, and 6.7%, respectively, in the 1 vol.%SiC/AA2024 nanocomposite sample. Analysis of the deformation behavior shows that this improvement is likely caused by the increased density of geometrically necessary dislocations (GNDs) resulting from the bimodal microstructure. The dispersed intragranular Sʹ precipitates generated by the T6 heat treatment also make a contribution to the increase of strength and ductility by accumulating dislocations. It is feasible to realize simultaneous improvement of strength and ductility in the SiC/AA2024 nanocomposites via powder metallurgy and subsequent heat treatment.

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Microstructural characteristics and tensile properties of nano-composite Al 2014/4wt.% Al2O3 produced from machining chips

Cited by 24 publications

References 23 publications

Solid-state recycling of aluminium alloy swarf into c-channel by hot extrusion

Solid-state recycling of aluminium alloy swarf into c-channel by hot extrusion

Prediction of hardness and electrical properties in ZrB2 particle reinforced metal matrix composites using artificial neural network

Investigation of Microstructures and Mechanical Properties of SiC/AA2024 Nanocomposites Processed by Powder Metallurgy and T6 Heat Treatment

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