Rice husk ash reinforced aluminium matrix composites: fabrication, characterization, statistical analysis and artificial neural network modelling

Shaikh, Mohd Bilal Naim; Raja, Sufian; Ahmed, Mukhtar; Zubair, Maria; Khan, Arfaat; Ali, Mohammed

doi:10.1088/2053-1591/aafbe2

Cited by 35 publications

(19 citation statements)

References 62 publications

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“…As the increasing concentration of CES decreased hardness of the composites, which implies that less contact of eggshells correspond to less wear at low loads, whereas more contact of eggshell particles at higher loads led to more wear than that of the unreinforced composite. Reduction in the wear rate with increased hardness was also reported by Singh et al, 51 Shaikh et al, 52 and Haq and Anand. 14 Moreover, from Figure 7(b), it can be inferred that comparative increase in porosity content was more as the concentration of CES increased, which may be another reason for enhanced wear loss after sample V1.…”

Section: Tribological Characterization Wear Characteristicssupporting

confidence: 73%

“…Higher wear loss at higher loads can be attributed to an increase in the contact area/pressure between mating surfaces, which ultimately leads to increased plastic deformation/delamination and, hence, more wear debris in comparison to wear debris produced at low loads due to the reduced actual area of contact between asperities of workpiece and counterface steel disc. 37,52 Moreover, there is an increase in the frictional force between mating surfaces with increment in load, which further promotes plastic deformation and ultimately decohesion/debonding of particles from the cast samples lead to more wear loss. 22,26,28 During sliding, there is a formation of thick tribo-layer between mating surfaces.…”

Section: Tribological Characterization Wear Characteristicsmentioning

confidence: 99%

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Tribological characteristics of AA7075 composites reinforced with rice husk ash and carbonized eggshells

Gupta

Singh

Mishra

2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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This paper investigates dry sliding friction and wear behavior of AA7075 composites reinforced with rice husk ash and carbonized eggshells. Composites with varying weight percentages of rice husk ash and carbonized eggshells in the range of 0–5 wt.% were fabricated through stir-casting technique. Density, porosity content, and microhardness were computed before tribological testing. Friction and wear tests were conducted on pin-on-disc type tribometer at room temperature. The samples were tested at different loads (10–50 N) with constant speed of 1 m/s and constant sliding distance of 1500 m. Samples were also tested at different sliding speeds (3 and 5 m/s) with constant normal load of 30 N. The addition of natural reinforcements decreased the density of composites. Microhardness of sample having 5 wt% rice husk ash increased by 15.08% over base composition. Maximum wear resistance was shown by sample with 5 wt% rice husk ash. Highest coefficient of friction was shown by sample with 3.75 wt% rice husk ash and 1.25 wt% carbonized eggshells. Wear loss varied directly with increasing load and sliding speeds for all composites, whereas coefficient of friction increased with increasing load and decreased with increasing sliding speed for all composites. Delamination and ploughing are dominant wear mechanisms at low speed whereas ploughing is the dominant wear mechanism at high speed as depicted by scanning electron microscopic images of worn surfaces. Composites with improved wear-resistant properties can be used for different tribological applications particularly in automotive industry.

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Section: Tribological Characterization Wear Characteristicssupporting

confidence: 73%

Section: Tribological Characterization Wear Characteristicsmentioning

confidence: 99%

Tribological characteristics of AA7075 composites reinforced with rice husk ash and carbonized eggshells

Gupta

Singh

Mishra

2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…However, the addition of 8 wt % RHA resulted in an increase of COF compared to the other variants for the two highest loads (39.22 N and 49.03 N). The addition of RHA into the aluminum matrix was also used by Shaikh et al [ 42 ]. Researchers observed the increase in the sliding wear resistance of the prepared composites in all variants containing RHA, with the highest concentration (15 wt %) being less effective than the other two studied contents (5 and 10 wt %).…”

Section: Agricultural Waste Materialsmentioning

confidence: 99%

Tribological Performance of Composites Reinforced with the Agricultural, Industrial and Post-Consumer Wastes: A Review

Sydow

Wojciechowski

et al. 2021

Materials

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Waste management is still one of the leading global challenges in the 21st century. From the European Union’s point of view, the Waste Framework Directive obliges businesses and households to recycle at least 55% of their municipal waste by 2025 and to reach 65% in 2035. Hence there is a great need to seek new solutions for the reuse of various waste materials. One of the most widely used wastes is their utilization as fillers or reinforcements in the metal- or polymer-based composites. The reuse of wastes for the production of tribological materials gives not only environmental benefits related to the transformation of waste into raw materials but also may improve the mechanical and tribological properties of such materials. Moreover, the use of waste reduces the production costs resulting from the lower price of filler materials and longer service life of developed products. The purpose of the current review is, therefore, aimed at the evaluation of the reuse of agricultural, industrial and postconsumer wastes as reinforcements in the composites used for tribological applications. The tribological performance (wear rate, coefficient of friction) of both monolithic and hybrid composites reinforced with waste materials was a particular subject of interest in this review.

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“…Performing experiments repeatedly is very time consuming; so, to avoid this problem various efficient and effective techniques such as genetic algorithm, artificial neural network, and response surface method, are being used. 11 Shaikh et al, 12 by powder metallurgy, reinforced aluminum with rice husk. The design of the experiment was developed using Taguchi, taking sliding speed, sliding distance, and applied load as control factors for the study.…”

Section: Introductionmentioning

confidence: 99%

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

Butola

Singari

Murtaza

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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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.

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Rice husk ash reinforced aluminium matrix composites: fabrication, characterization, statistical analysis and artificial neural network modelling

Cited by 35 publications

References 62 publications

Tribological characteristics of AA7075 composites reinforced with rice husk ash and carbonized eggshells

Tribological characteristics of AA7075 composites reinforced with rice husk ash and carbonized eggshells

Tribological Performance of Composites Reinforced with the Agricultural, Industrial and Post-Consumer Wastes: A Review

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

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