Microstructure, texture, and mechanical properties analysis of novel AA7178/SiC nanocomposites

Bharat, Nikhil; Bose, P.S.C.

doi:10.1016/j.ceramint.2023.03.195

Cited by 16 publications

(1 citation statement)

References 42 publications

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“…Hybrid MMCs [1] are developed by adding more than one reinforcement material to achieve the desired properties. Ceramic reinforcements such as Al 2 O 3 , SiC, TiO 2 , etc, will increase the hardness and wear resistance [2]. The wear resistance leads to poor machinability characteristics.…”

Section: Introductionmentioning

confidence: 99%

Assessment of machinability behaviour of LM24 – nano Al₂O₃ – gr hybrid composites through stir casting technique

Surendran,

Kumaravel

2024

Mater. Res. Express

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This study investigated the machinability characteristics of the turning operation to attain the optimal combination of minimum surface roughness and the highest material removal rate. The analysis employed Response Surface Methodology (RSM). The study conducted a series of experimental runs using the Box-Behnken Design of Response Surface Methodology. The parameters of spindle speed, feed, and depth of cut were varied, while different weight percentages of reinforcements, including Al2O3 nanoparticles (at 1%, 3%, and 5% weight percentages) and Gr (at 1% weight percentage), were incorporated into the LM24 metal matrix alloy. Response Surface Methodology (RSM) analysis is carried out to optimize the turning machining parameters and enhance the machining performance for better quality and productivity. The derived optimal machining parameters have been verified through confirmatory tests. An analysis of variance (ANOVA) was used to determine the individual contributions of each parameter to the machinability characteristics. Surface morphology analysis revealed the smooth distribution of nanoparticle reinforcements to the metal matrix. The hardness and surface roughness of the machined nanocomposites were studied. Finally, the optimized output machining parameter of minimum surface roughness Ra and maximum material removal rate MRR are 0.522 μm and 110.2 mm3/sec were to be found, and the maximum hardness of the MMC composites was 60.5 HRB.

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Section: Introductionmentioning

confidence: 99%

Assessment of machinability behaviour of LM24 – nano Al₂O₃ – gr hybrid composites through stir casting technique

Surendran,

Kumaravel

2024

Mater. Res. Express

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Studies of SiC‐Filled Al6061 Metal Matrix Composite Optical, Mechanical, Tribological, and Corrosion Behavior with Strengthening Mechanisms

Mantha,

Veeresh Kumar,

Pramod

et al. 2024

Adv Eng Mater

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The objective of the current study is to produce metal matrix composites (MMCs) using ultrasonic‐assisted stir casting and Al6061 alloy reinforced with silicon carbide (SiC) microparticle reinforcement in weight percentages of 0, 2, 4, and 6. The microstructural alterations of Al6061–SiC composites are investigated using a scanning electron microscope (SEM) equipped with an energy‐dispersive X‐ray (EDAX). By adding more nucleation sites for the formation of smaller grains, SiC reinforcement of the Al6061 matrix encourages grain refining. The SiC addition significantly changes the microstructure of Al6061 composites, enhancing their mechanical qualities. In addition to increasing density by 0.6%, hardness by 33%, and tensile strength by 33%. The increased SiC content dramatically decreases elongation by 42%. The strength of Al6061–SiC MMCs is predicted using several strengthening mechanism concepts as part of the continuing investigation. For Al6061–SiC composites, the strengthening contribution from thermal mismatch is more significant than that from Orowan strengthening, Hall–Petch mechanism, and load transmitting effect. Grain refinement interactions, load transmission mechanisms, and the strengthening effects of CTE differences and dislocations between matrix and reinforcement particles are studied. The composite with 6‐weight percent SiC reinforcement performs better in dry sliding wear and corrosion resistance.

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Integrating Genetic Algorithms, Particle Swarm, and Neural Networks for Wear Optimization of AA7178 Matrix with Nano-SiC Particles

Bharat,

Bose

2023

JOM

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Microstructure, texture, and mechanical properties analysis of novel AA7178/SiC nanocomposites

Cited by 16 publications

References 42 publications

Assessment of machinability behaviour of LM24 – nano Al₂O₃ – gr hybrid composites through stir casting technique

Assessment of machinability behaviour of LM24 – nano Al₂O₃ – gr hybrid composites through stir casting technique

Studies of SiC‐Filled Al6061 Metal Matrix Composite Optical, Mechanical, Tribological, and Corrosion Behavior with Strengthening Mechanisms

Integrating Genetic Algorithms, Particle Swarm, and Neural Networks for Wear Optimization of AA7178 Matrix with Nano-SiC Particles

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Microstructure, texture, and mechanical properties analysis of novel AA7178/SiC nanocomposites

Cited by 16 publications

References 42 publications

Assessment of machinability behaviour of LM24 – nano Al2O3 – gr hybrid composites through stir casting technique

Assessment of machinability behaviour of LM24 – nano Al2O3 – gr hybrid composites through stir casting technique

Studies of SiC‐Filled Al6061 Metal Matrix Composite Optical, Mechanical, Tribological, and Corrosion Behavior with Strengthening Mechanisms

Integrating Genetic Algorithms, Particle Swarm, and Neural Networks for Wear Optimization of AA7178 Matrix with Nano-SiC Particles

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Assessment of machinability behaviour of LM24 – nano Al₂O₃ – gr hybrid composites through stir casting technique

Assessment of machinability behaviour of LM24 – nano Al₂O₃ – gr hybrid composites through stir casting technique