Tribological Properties of Fly Ash Blended Polymer Composites

Güneş, İbrahim; Uygunoğlu, Tayfun; Çelik, Atila Gürhan

doi:10.1590/s1517-707620210001.1229

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…At 6 N µ was high perhaps due to tearing of the fibre tribo layer at the contact region. This finding was contrary to results obtained by 39 . K s was low at high load because of great increase in apparent contact area at higher loads thereby leading to increase in contact area permitting a large number of particles to meet the interface and share the stress.…”

Section: Effect Of Load On Ks and µcontrasting

confidence: 99%

Multi-Response Optimization and Prediction of Tribological behaviours of Filled Polytetrafluoroethylene Composites Using Taguchi-Deng and Evolutionary Harris Hawk’s Optimization Methodologies

Ibrahim

Çamur

Savas

et al. 2022

Preprint

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Filled polymer composites are widely used for automobile, structural and aerospace components owing to their exceptional combination of high specific stiffness and strength. This study presents Taguchi-Deng optimization of tribological parameters such as load, grit size, distance and speed as well as prediction of tribological behaviours of carbon-filled and bronze-filled polytetrafluoroethylene (PTFE) composites using pin on disk configuration. A plan of experiments based on Taguchi L27 (43) orthogonal array (OA) was designed to collect data in a controlled manner. The Taguchi L27 (43) was hybridized with Deng model to produce grey relational grades (GRG) for the multiple response optimization. Analysis of variance (ANOVA) was executed to establish the parameters affecting GRG of the composites. For the prediction of the tribological behaviours of the composites namely coefficient of friction (µ) and specific wear rate (Ks), support vector regression (SVR) was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Prediction accuracy of the models were appraised using coefficient of determination (R2), correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage error (MAPE). GRG results revealed that optimum parameters which reduced tribological behaviours were factor combination L3G1D3S3. ANOVA for GRG reveled that grit size with 68.57% ranks as the most influential parameter followed by load with 20.57%, followed by distance having a contribution of 7.78% and finally speed with least contribution of 3.38% for minimum tribological loss. Validation performed using optimum parameters revealed an enhancement of 55% in GRG. Prediction accuracy of the single model increase to 19.50% and 57.08% on the average for hybrid µ and Ks models, respectively. Furthermore, SVR-HHO model indicated the higher prediction accuracy of the tribological behaviours of filled PTFE composites as compared to SVR-PSO model. These findings concluded these metaheuristic models are promising in predicting tribological behaviours of filled PTFE composites and thus can serve as a guide in the design and development of tribological materials.

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Section: Effect Of Load On Ks and µcontrasting

confidence: 99%

Multi-Response Optimization and Prediction of Tribological behaviours of Filled Polytetrafluoroethylene Composites Using Taguchi-Deng and Evolutionary Harris Hawk’s Optimization Methodologies

Ibrahim

Çamur

Savas

et al. 2022

Preprint

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“…Also, wear debris is transferred from the matrix leading to reduced wear rate. Similar findings were reported by [25] and [26] when nylon 6 was reinforced with glass fibre at varied proportions.…”

Section: Effect Of Sliding Distance On µ and A Wsupporting

confidence: 88%

Multi-Response Optimization of the Tribological Behaviour of PTFE-Based Composites via Taguchi Grey Relational Analysis

Ibrahim¹,

Çamur²,

Savas³

et al. 2022

sv-jme

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Polymer-based composites find applications in several areas because of their exceptional properties. This article deals with Taguchi grey relational optimization method of abrasive parameters (load (L), grit size (G) and sliding distance (D)) and their influence on abrasive performance of reinforced polytetrafluoroethylene (PTFE) composites. A Taguchi L9 orthogonal array was designed, and nine experimental tests were conducted based on the Taguchi designed experiments. A pin-on-disc tribology machine was used for the experiments. The coefficient of friction (µ) and abrasive specific wear rate (Aw) were recorded for each experiment. An analysis of variance (ANOVA) was performed to establish the significance and percentage contribution of each parameter affecting the abrasive wear performance. Results from the Taguchi-grey-relational method showed that the optimal combination of parameters was achieved at load of 10 N, grit size of 1000 mesh, and sliding distance of 350 m (coded as L3G1D3). ANOVA findings revealed that a grit size with 67.69 % as the most influential on the abrasive performance of polymer-based composites. Validation tests performed using the optimal combination parameter showed an enhancement of 55.22 % in grey relational grade.

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“…This mechanical behavior is owed to the mechanical strength of fly ash, its rigidity, and especially due to its high SiO 2 content, which exhibits a high affinity for the rubber matrix and thus extending the contact area. There are reports on fly ash’s high affinity for rubber matrices [ 37 , 38 ]; thus, hybrid rubber–fly ash interface development is likely to be developed. It is worthy to note that the 160 °C sample recorded the best compression increase after the addition of FA.…”

Section: Resultsmentioning

confidence: 99%

The Influence of Fly Ash on the Mechanical Properties of Water Immersed All Waste Composites

2022

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The paper presents new value-added composite materials prepared by recycling tire rubber, polyethene terephthalate (PET), high-density polyethene (HDPE), wood sawdust, and fly ash. The composites were manufactured through the compression molding technique for three temperatures (150 °C, 160 °C, and 190 °C) previously optimized. The addition of fly ash as reinforcement in polymer blends is a viable route to improve the composite” properties. The paper aims to assess the effect of fly ash on the mechanical properties and water stability of the new all waste composites considering their applications as outdoor products. The static tensile (stress-strain behavior) and compression properties of the composites were tested. The fly ash composites were characterized in terms of wetting behavior and surface energies (contact angle measurements); chemical structure of the new interface developed between composite” components (FTIR analysis), crystalline structure (XRD analysis), surface morphology and topography (SEM, AFM). The addition of fly ash promoted the development of the hybrid interfaces in the new composites, as FTIR analysis has shown, which, in turn, greatly improved the mechanical and water resistance. The novel all waste composites exhibited lower surface energies, larger contact angles, and smoother morphologies when compared to those with no fly ash. Overall, the study results have revealed that fly ash has improved the mechanical strength and water stability of the composites through the formation of strong hybrid interfaces. The study results show optimal water stability and tensile strength for 0.5% fly ash composites cured at 190 °C and optimal compressive strength with good water stability for 1% fly ash composite cured at 150 °C.

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Tribological Properties of Fly Ash Blended Polymer Composites

Cited by 12 publications

References 24 publications

Multi-Response Optimization and Prediction of Tribological behaviours of Filled Polytetrafluoroethylene Composites Using Taguchi-Deng and Evolutionary Harris Hawk’s Optimization Methodologies

Multi-Response Optimization and Prediction of Tribological behaviours of Filled Polytetrafluoroethylene Composites Using Taguchi-Deng and Evolutionary Harris Hawk’s Optimization Methodologies

Multi-Response Optimization of the Tribological Behaviour of PTFE-Based Composites via Taguchi Grey Relational Analysis

The Influence of Fly Ash on the Mechanical Properties of Water Immersed All Waste Composites

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