Optimizing the ultrasonication effect in stir-casting process of aluminum hybrid composite using desirability function approach and artificial neural network

2021 ◽  
pp. 146442072110257
Author(s):  
Logesh Kamaraj ◽  
P Hariharasakthisudhan ◽  
A Arul Marcel Moshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Ultrasonic Vibration ◽  
Desirability Function ◽  
Stir Casting ◽  
Ultrasonic Assisted ◽  
Ultrasonic Assisted Stir Casting ◽  
Vibration Time ◽  
Desirability Function Approach

The ultrasonic-assisted stir-casting technique improves the uniform dispersion of nano-reinforcements in aluminum hybrid metal matrix composites. In the present study, the process parameters of the ultrasonic-assisted stir-casting method, such as ultrasonic vibration time, and depth of ultrasonic vibration along with the speed of mechanical stirrer, are optimized on A356 hybrid composite material optimally reinforced with aluminum nitride, multiwalled carbon nanotubes, graphite particles, and aluminum metal powder using the desirability function approach. The process parameters are optimized against the response factors such as porosity, ultimate tensile strength, and wear rate of the composites. The optimum combination of input factors is identified as stirring speed (600 r/min), ultrasonic vibration time (2 min), and depth of ultrasonic vibration (40 mm) among the selected range. The corresponding output response values are found to be porosity (1.4%), ultimate tensile strength (247 MPa), and wear rate (0.0013 mm3/min). The ANOVA results have revealed that depth of ultrasonic vibration showed significant contribution among the input factors. An artificial neural network model is developed and validated for the given set of experimental data.

scholarly journals Microstructure evolution, mechanical properties, and fractography of AA7068/ Si3N4 nanocomposite fabricated thorough ultrasonic-assisted stir casting advanced with bottom pouring technique

2022 ◽  
Author(s):  
Ashish Kumar ◽  
R. S. Rana ◽  
Rajesh Purohit
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Fine Particles ◽  
Base Alloy ◽  
High Strength ◽  
Stir Casting ◽  
Microstructural Investigation ◽  
Metal Matrix Nanocomposites ◽  
Ultrasonic Assisted ◽  
Ultrasonic Assisted Stir Casting

Abstract Ceramic particulate embedded aluminum metal matrix nanocomposites (AMNCs) possess superior mechanical and surface properties and lightweight features. AMNCs are a suitable replacement of traditional material, i.e., steel, to make automotive parts. The current work deals with developing Si3N4 strengthened high strength AA7068 nanocomposites via novel ultrasonic-assisted stir casting method advanced with bottom pouring setup in the proportion of 0.5, 1.0, 1.5, and 2 wt.%. Planetary ball milling was performed on a mixture of AA7068 powder and Si3N4 (in the proportion of 3:1) before incorporation in aluminum alloy melt to avoid rejection of fine particles. Finite element scanning electron microscope (FESEM), Energy dispersive spectroscopy (EDS), X-Ray diffraction (XRD), and Elemental mapping techniques were used in the microstructural investigation. Significant grain refinement was observed with increasing reinforcing content, whereas agglomeration was found at higher weight %. Hardness, Tensile strength, ductility, porosity content, compressive strength, and impact energy were also examined of pure alloy and each composite. Improvement of 72.71%, 50.07%, and 27.41 % was noticed in hardness value, tensile strength, and compressive strength, respectively, at 1.5 weight % compared to base alloy because of various strengthening mechanisms. These properties are decreased at 2wt.% due to severe agglomeration. In contrast, nanocomposite's ductility and impact strength continuously decrease compared to monolithic AA7068. Fracture analysis shows the ductile and mixed failure mode in alloy and nanocomposites.

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

2021 ◽  
pp. 095440892110368
Author(s):  
Ravi Butola ◽  
Ranganath M. Singari ◽  
Qasim Murtaza ◽  
Lakshay Tyagi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Rotational Speed ◽  
Total Elongation ◽  
Tool Rotational Speed ◽  
Friction Stir ◽  
Artificial Neural

In the present work, nanoboron carbide is integrated in the aluminum matrix using friction stir processing: by varying process parameters, that is, tool pin profile, tool rotational speed and tool traverse speed, based on Taguchi L16 design of experiment. A self-assembled monolayer is successfully developed on the substrate to homogeneously and uniformly distribute the reinforcement particles. Response surface methodology and artificial neural network models are developed using ultimate tensile strength and total elongation as responses. Percentage absolute error between the experimental and predicted values of ultimate tensile strength and total elongation for the response surface methodology model is 3.537 and 2.865, respectively, and for artificial neural network is 2.788 and 2.578, respectively. For both the developed models experimental and forecasted values are in close approximation. The artificial neural network model showed slightly better predictive capacity compared to the response surface methodology model. From the scanning electron microscopy micrograph, it is evident that throughout the matrix B4C reinforcement particles are well distributed also; with increasing tool rotational speed grain size decreases up to 1200 r/min; on further increasing the tool rotational speed particles starts clustering.

Artificial Neural Network based Prediction of Tensile Strength of Hybrid Composites

2018 ◽  
Vol 5 (9) ◽  
pp. 19908-19915 ◽  
Author(s):  
M. Gayatri Vineela ◽  
Abhishek Dave ◽  
Phaneendra Kiran Chaganti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Hybrid Composites ◽  
Artificial Neural

New Emerging Al7075 Based Hybrid Nanocomposite for Automotive Applications: A Sustainability Approach

2020 ◽  
Vol 856 ◽  
pp. 29-35
Author(s):  
Sweety Mahanta ◽  
M. Chandrasekaran ◽  
Sutanu Samanta
Keyword(s):  
Tensile Strength ◽  
Impact Strength ◽  
Hybrid Composites ◽  
High Strength ◽  
Stir Casting ◽  
Automotive Application ◽  
Percentage Increase ◽  
Casting Technique ◽  
Ultrasonic Assisted Stir Casting ◽  
The Impact

Aluminium matrix composites (AMCs) have emerged as the substitute for the monolithic (unreinforced) materials over the past few decades. The applications of AMCs are common in automotive, aerospace, defence and biomedical sectors due to its lower weight, high strength, high resistance against corrosion and high thermal and electrical conductivity. In this work, it is aimed fabricate a new class Al 7075 based hybrid composites reinforcing with nanoparticulates suitable for automotive application. Al7075 reinforced with fixed quantity of boron carbide (B4C) (1.5 wt.%) and varying wt % of flyash (0.5 wt.%, 1.0 wt.%, 1.5 wt.%) is fabricated using ultrasonic-assisted stir casting technique. Physical and mechanical characterization such as density, porosity, micro hardness, tensile strength and impact strength were estimated for three different compositions. The tensile strength and percentage increase in hardness value of the nanocomposite Al7075-B4C (1.5 wt. %)-flyash (0.5 wt. %): HNC3 found maximum as 294 MPa and 32.93%. In comparison with Al7075 alloy the impact strength of HNC3 shows the highest percentage of 9.31% respectively.

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