Optimization of Mechanical Properties of AA 5083 Nano SiC Composites using Design of Experiment Technique

Rana, R.S.; Purohit, Rajesh; Mishra, Pushyamitra; Sahu, Pramod Kumar; Dwivedi, Shailendra

doi:10.1016/j.matpr.2017.02.287

Cited by 9 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their micro-structural assessment showed unvarying distribution of nano SiCp in the MMC as well as robust bonding in between the particle and matrix at the interface. Rana et al [20] optimized the mechanical properties of AA5083 silicon carbide nano composites material by the application of DOE (Design of Experiment). The effect of process parameters was tested on the outputs.…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental investigation into characterization and machining of Al + SiCp nano-composites using coated carbide tool

Swain¹,

Mohapatra²,

Das³

et al. 2020

Mechanics & Industry

View full text Add to dashboard Cite

The current research paper discusses the characterization and machining (turning) operation of aluminium (Al) and silicon carbide nano particle (SiCp) nano composite. The paper reveals proper distribution of silicon carbide nano particles (25nm) with aluminium metal matrix. Initially, tensile test has been carried on metal matrix nano composite to study its different properties. It was noticed that the properties of Al-SiCp increases by increasing the weight percentage of SiCp. Viker hardness test has also been conducted to find out the hardness of metal matrix nano composites. Different techniques i.e. Optical microscopy EDX-Analysis were utilized to find out various ingredients of the nano-composite material. The experimental study was carried out using Taguchi L 16 orthogonal array by taking three different factors at four different levels each. The response parameters i.e. flank wear of coated carbide insert and surface roughness of Al-SiCp has been optimized by using Principal Component Analysis (PCA). Various graphs like main effect plot and normal probability plot have been plotted and studied properly. Different optical images of coated insert carbide tools (Insert CNMG 12040822TN 6010) at different runs were conducted to visualize the effects of process and response parameters. From the ANOVA table, it was found that cutting speed as well as depth of cut are found to the most vital parameters in influencing the responses for VBc and depth of cut and feed are found to the most significant parameters in influencing the responses for Ra.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Experimental investigation into characterization and machining of Al + SiCp nano-composites using coated carbide tool

Swain¹,

Mohapatra²,

Das³

et al. 2020

Mechanics & Industry

View full text Add to dashboard Cite

show abstract

“…Among the four parameters, volume fraction and counter-face hardness had the most influence on reduction of wear rate of the hybrid composites. Rana et al, (2017) developed a mathematical model to study the influence of process parameters (casting temperature, stirrer speed, and weight percent of reinforcement) on hardness of AA5083/Nano-SiC composite fabricated by stir casting. Optimum hardness of 19.4 HBN was obtained using the optimized process parameters 2wt.% of nano-SiC, 760 °C casting temperature and 550 rpm stirrer speed generated from the model.…”

Section: Introductionmentioning

confidence: 99%

Study on the Optimization of Surface Modification Processing of SiCp and Tribological Properties of AA6061-SiCp based Composites

Odiwo

Adeyemi

Abdulwahab

et al. 2023

IJEMM

View full text Add to dashboard Cite

The unique property combination of Al/SiCp based composites make them very attractive for applications in automotive and aerospace industries. The choice of composite materials for these applications is directly influenced by their inherent properties which are a function of the processing route employed. Like other processing parameters, surface modification treatment of SiCp can play a major role in determining the properties of Al/SiCp composites. In this study, the effects of SiC reinforcement (wt%) fractions (SRF), surface oxidation temperature (SOT) and preheating temperature (PT) parameters on the wear and friction properties of stir-cast Al-SiCp based composite were investigated. Experimental data and models are generated and analyzed based on a three-factors-five-level central composite design (CCD) and analysis of variance (ANOVA). The empirical models developed for wear rate and coefficient of friction (COF) considering the pre-processing parameters adequately predicts the Al-SiCp properties with the silicon carbide reinforcement (wt%) fraction emerged as the most influencing factor. The goal of the optimization process is to minimize both wear rate and COF. For wear rate, SRF at 44.49 % contribution had the most influence on wear rate, while SOT and PT had 0.65 % and 1.03 % influence on wear rate respectively. For COF, SRF also showed highest influence of 35.48 % on COF, while SOT and PT had 0.047% and 2.66% influence on COF respectively. From the optimization analysis, the set of conditions that simultaneously optimizes both wear rate and COF are 10% SiC weight (SW), 1234°C surface oxidation temperature (SOT), and 376.2°C preheat temperature (PT). The resulting responses at this optimized condition are minimum wear rate of 0.11 mm3/m and COF of 0.11 with a confidence and desirability level of 1.

show abstract

“…Shabani and Mazahery proposed a hybrid genetic algorithm/particle swarm optimization algorithm to efectively estimate the optimal process conditions for preparing nanocomposites by casting [5]. Rana et al tested the infuence of each parameter on the responsiveness and sufciency of the hardness model by variance analysis and Fisher f test, and the mathematical model afecting the hardness of composite materials was established [6]. Sridhar et al, through this numerical investigation, estimated the temperature distribution profles by fnite element program (ANSYS) for diferent composition of composites [7].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation on Impeller Casting Defects and Optimization

Liu

Jia

Zhou

et al. 2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The numerical simulation is similar to the experimental research, which uses the computer. 3D printing is an experimental study, which is compared with simulation. 3D printing and precision casting of impellers are frequently used in industrial scenarios. To address the problem of low casting yield of aluminium alloy impellers in gypsum-type casting, three-dimensional modelling by magics, casting simulation, and parameter optimization method by applying ProCAST software are used to improve the casting yield of impellers. The results show that the optimized structure of the casting system is more reasonable, so that the shrinkage and shrinkage defects are concentrated in the location of the riser; the maximum stress value of the casting and the casing during the solidification stage is 2.71 MPa, which is much smaller than the stress limit of the casing; the maximum horizontal displacement of the casting along the X and Y axes after solidification is completed at 0.025 mm and 0.022 mm, respectively, and the maximum displacement along the Z axis due to the presence of the riser is 0.009 mm, which greatly improves casting accuracy and molding quality.

show abstract

Optimization of Mechanical Properties of AA 5083 Nano SiC Composites using Design of Experiment Technique

Cited by 9 publications

References 10 publications

Experimental investigation into characterization and machining of Al + SiCp nano-composites using coated carbide tool

Experimental investigation into characterization and machining of Al + SiCp nano-composites using coated carbide tool

Study on the Optimization of Surface Modification Processing of SiCp and Tribological Properties of AA6061-SiCp based Composites

Numerical Simulation on Impeller Casting Defects and Optimization

Contact Info

Product

Resources

About