Influence of AlN particles on microstructure, mechanical and tribological behaviour in AA6351 aluminum alloy

Mohanavel, V.; Ravichandran, M.

doi:10.1088/2053-1591/ab39b0

Cited by 168 publications

(21 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the total, the transportation sector consumes 23%, followed by construction (25%), packing (8%) and engineering (11%), and electrical appliances (6%). is metal has a low density, high malleability, resistance to corrosion, and electrical conductivity [4,5].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite

Mahesha

Suprabha

Bhavani

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Improved properties can be found in aluminum alloys containing silicon carbide reinforcement particles. This work studies the machinability of Al 6063 reinforced with silicon carbide particles with wire electrical discharge machining. To attain a high material removal rate, wire EDM constraints such as current (I), pulse-on time (Ton), wire speed (Ws), voltage I v , and pulse-off time (Toff) can be adjusted with precision. Taguchi L16 orthogonal arrays are used to design the experiments and statistical methods are used to examine. These process characteristics had a significant impact on the overall rate of return, with a 28.2% impact on the MRR, 23.04% impact on the MRR, and 22.86% impact on the MRR. We achieved MRRs of 65.21 mg/min for samples containing 5% and 10% SiCp at optimal conditions, respectively. Linear regression was used to create the statistical model, which then used confirmation trials to verify its accuracy in predicting MRR (R -73.65%). The statistical model is used to estimate MRR based on various process parameter settings.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite

Mahesha

Suprabha

Bhavani

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Carbonates, oxides (Al 2 O 3 and SiO 2 ), and nitrides are some of the ceramic materials used in reinforcing (Al 2 O 3 and Si 3 N 4 ). AA2618-based metal matrix composites (MMCs) may be made using these reinforcements [1][2][3][4][5][6]. Excellent mechanical qualities such as TS, compression strength, and wear resistance are just few of MMC's advantages [7].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Parameters of Zirconium Carbide and Rice Husk Ash Reinforced with AA 2618 Composites

Mahesha

Suprabha

Thenmozhi

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Stir casting was utilized to generate the composites. The AA 2618–10wt% zirconium carbide 10wt% rice husk ash hybrid composite had the highest hardness value. In comparison to pure AA 2618, it had a 72% increase. The rule of mixture was used to compute theoretical density, whereas the Archimedes rule was utilized to evaluate real density. For AA 2618, AA 2618/zirconium carbide, and rice husk ash hybrid composites, simple carbide inserts were used for turning. Surface roughness, metal removal rate, and tool wear were among the numerous responses examined. RSM was utilized to analyze and optimize the test outcomes. Feed rate was the most critical issue for surface roughness and material removal rate. Tool wear was shown to be most strongly controlled by tool speed, whereas metal removal rate was found to be least significantly influenced by the weight percent of reinforcement.

show abstract

“…AlN is a normally necessary strengthening material for AMCs as it gets the compelling mixing of physical, tribological, and mechanical attributes like better hardness, less density, good versatile modulus, and remarkable wear obstruction. Particularly, usage of AlN particles as fortification has consistently upgraded the AMC mechanical properties [14]. AlN reinforced with aluminum is nonreactive; AlN possesses better hardness, better thermal conductivity, and least coefficient of thermal expansion [15].…”

Section: Introductionmentioning

confidence: 99%

Tribological Behavior of Al/Nanomagnesium/Aluminum Nitride Composite Synthesized through Liquid Metallurgy Technique

Srinivasan¹,

Meignanamoorthy²,

Gacem

et al. 2022

Journal of Nanomaterials

View full text Add to dashboard Cite

Despite its excellent qualities such as hardness, tensile, and yield strength, aluminum alloys are mostly used in aviation fins and car frames. However, wear resistance at maximum load is weak. This effort will now synthesize and investigate the tribological behavior of AA6063- (AlMg0.7Si-) AlN composites. The goal of this experiment is to determine the best wear rate and coefficient of friction for the AA6063-AlN with nanomagnesium composites developed. Weight percent, load (L), sliding velocity (SV), and sliding distance (SD) are the process factors studied, and the output responses are wear rate and friction coefficient. Bottom pouring type stir casting was used to create AA6063-AlN composites with various weight percentages. The various compositions are AA6063, AA6063-4 wt% AlN, AA6063-8 wt% AlN, and AA6063-12 wt% AlN. A pin-on-disc machine inspected the wear rate and friction coefficient of AA6063-AlN composites. Experimentation was done according to L16 orthogonal array (OA). Wear rate (WR) and coefficient of friction (COF) examinations were made to identify the optimum parameters to obtain minimum WR and COF for the AA6063-AlN composite via grey relational analysis (GRA). The contour plot analysis clear displays WR and COF with respect to wt% vs. L, wt% vs. SV and wt% vs. SD. The ANOVA outcomes revealed that wt% is the most vital parameter (85.55%) persuading WR and COF. The optimized parameters to achieve minor WR and COF was found as 12 wt% of AlN, L 20 N, SV 3 m/s, and SD 400 m. The worn surface was analyzed using scanning electron microscope and indicates that addition of AlN particles with matrix reduces the scratches. These articles offer a key for optimum parameters on wear rate and COF of AA6063-AlN composites via Taguchi grey relational analysis.

show abstract

Influence of AlN particles on microstructure, mechanical and tribological behaviour in AA6351 aluminum alloy

Cited by 168 publications

References 51 publications

Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite

Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite

Optimizing the Parameters of Zirconium Carbide and Rice Husk Ash Reinforced with AA 2618 Composites

Tribological Behavior of Al/Nanomagnesium/Aluminum Nitride Composite Synthesized through Liquid Metallurgy Technique

Contact Info

Product

Resources

About