Experimentally Predicted Optimum Processing Parameters Assisted by Numerical Analysis on the Multi-physicomechanical Characteristics of Coir Fiber Reinforced Recycled High Density Polyethylene Composites

Awodi, E; Us, Ishiaku; Mk, Yakubu; Jk, Abifarin

doi:10.21203/rs.3.rs-591200/v1

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…The corresponding experimental hardness, compressive strength and resultant grey relational grade were analyzed using Taguchi on Minitab. The steps for generating the resultant grey relational grade for the experimental mechanical properties are shown in section 2.2, and are similar to the work of Abifarin (2021) and Awodi et al (2021). All the plotted graphs were obtained using Minitab.…”

Section: Taguchi Experimental Designmentioning

confidence: 99%

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Taguchi Grey Relational Optimization of the Multi-mechanical Characteristics of Kaolin Reinforced Hydroxyapatite: Effect of Fabrication Parameters

Abifarin

Olubiyi

Dauda

et al. 2021

IJGS

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Comparative study of kaolin reinforced hydroxyapatite (KHAp) and pure HAp using different production parameters has been done through traditional experimentation. However, the quantitative effect, optimization of kaolin reinforcement and fabrication parameters have not been investigated. Hence, this study examines the effect of kaolin reinforcement, compaction pressure and sintering temperature on the experimental mechanical properties of HAp. Taguchi design assisted by grey relational analysis was employed with L36 (2**2 3**1) orthogonal array. The Minitab 16 software was used to analyze the Taguchi design. The result showed a disparity in kaolin reinforcement as the optimum condition for individual mechanical properties, but the grey relational analysis showed better mechanical properties with kaolin reinforcement, 500 Pa compaction pressure and 1100 oC sintering temperature. The obtained result further revealed kaolin reinforcement as a strong and promising reinforcing material for high strength clinical application, having a contribution of 93.16% on compressive strength of HAp. Therefore, future studies can be conducted in the use of different wt% of kaolin on the multi-response mechanical characteristics of HAp.

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Section: Taguchi Experimental Designmentioning

confidence: 99%

“…To investigate the authenticity of the predicted response and experimental response, confidence interval (CI) was calculated using Equation 6 (Taguchi & Phadke, 1989;Abifarin, 2021;Awodi et al, 2021):…”

Section: Confirmation Analysis 371 Confidence Interval Analysismentioning

confidence: 99%

Taguchi Grey Relational Optimization of the Multi-mechanical Characteristics of Kaolin Reinforced Hydroxyapatite: Effect of Fabrication Parameters

Abifarin

Olubiyi

Dauda

et al. 2021

IJGS

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“…The GRG (the converted singular response) gives the overall multiple performance characteristics for the six experimental data, which made it possible for Taguchi DOE technique to analyze. As always require in GRA Optimization, the higher-the-better signal to noise ratio is considered for the Taguchi DOE analysis (Taguchi & Phadke, 1989;Taguchi et al, 2000;Taguchi et al, 2005;Abifarin, 2021;Abifarin et al, 2021b;Abifarin et al, 2022b;Awodi et al, 2021).…”

Section: Research Methodology and Data Analysismentioning

confidence: 99%

Modeling and Grey Relational Multi-response Optimization of Chemical Additives and Engine Parameters on Performance Efficiency of Diesel Engine

Abifarin

Ofodu

2022

IJGS

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Singular optimization of engine conditions for better engine performance have been studied extensively. However, in the practical sense, more than one performance characteristics are essential in the optimization of engine conditions. The current study investigates the effect, optimization, and modeling of engine conditions on multi-characteristics of a single cylinder-dual direct injection-water cooled diesel engine with the help of Taguchi-grey relational and regression analyses. The engine conditions employed are engine load, hydrogen, multi-walled carbon nanotubes (MWCNTs), ignition pressure, and ignition timing, at four different levels. The engine performance characteristics analyzed were brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), hydrocarbons (HC), nitrogen oxide (NOx), carbon monoxide (CO), and carbon dioxide (CO2). The results showed that there was a similar behavioral pattern of the effect of engine conditions on engine performance, except for ignition timing. The optimal settings for better engine performance were obtained at 25% engine load, 20% hydrogen, 50 ppm MWCNTs, 220 bar ignition pressure, and 21 obTDC ignition timing. Interestingly, the discovered optimal did not fall within the considered experimental runs, however, the predicted optimal engine performance was within 95% confidence bounds. It is recommended that the experimental work based on the obtained optimal settings should be conducted to elucidate the efficacy of the confirmation analysis. The analysis of variance showed that the engine load was the most significant factor on the overall engine performance, having a contribution of 71.47%, followed by hydrogen and MWCNTs. Also, the ignition pressure and timing were not significant on the overall engine performance, which showed a need to place more attention on the significant factors for better engine performance. The mathematical and graphical modeling showed the efficacy of the design analysis, while the interaction plots showed broader detailed factor settings for better engine performance.

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“…It is not blurred that several optimization techniques only solve a problem of optimization of a singular performance characteristic, such as Taguchi design technique, response surface methodology (RSM), etc. However, Grey relational analysis (GRA) has been proven to be effective in singularizing as many performance characteristics as possible for multi-objective optimization [17][18][19][20][21][22][23]. Hence, in this study, RSM is assisted by GRA to conduct multi-objective optimization analysis on consistency index (K), flow behavior index (n), yield point (YP), and plastic viscosity (PV) of water-based drilling muds.…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis on the Selection of High Efficient Biodegradable-PolymerBased Nanofluids for Drilling Mud

2022

JBTS

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Proper and optimal selection of processing conditions in the production of nanofluids for drilling muds is critical. In this study, grey relational analysis (GRA) was employed as the computation analysis assisted by mathematical and graphical modeling to determine some optimal processing parameters to produce highly efficient biodegradable-polymer-based nanofluids for drilling muds. The multiple performance characteristics analyzed are consistency index (K), flow behavior index (n), yield point (YP), and plastic viscosity (YP). The obtained results revealed that 0.6 wt% starch, 1 wt% nanoparticles and 45 min. ultrasonic time are the optimal settings to be considered in the production of highly efficient nanofluids for drilling muds. The graphical modeling and ANOVA results also revealed that the experimental setups were influenced by some significant and unconsidered external factors in drilling fluids formulation. It is recommended that deeper study should be conducted to identify and employ some other unconsidered factors that may be responsible on the performance of drilling muds.

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Experimentally Predicted Optimum Processing Parameters Assisted by Numerical Analysis on the Multi-physicomechanical Characteristics of Coir Fiber Reinforced Recycled High Density Polyethylene Composites

Cited by 9 publications

References 25 publications

Taguchi Grey Relational Optimization of the Multi-mechanical Characteristics of Kaolin Reinforced Hydroxyapatite: Effect of Fabrication Parameters

Taguchi Grey Relational Optimization of the Multi-mechanical Characteristics of Kaolin Reinforced Hydroxyapatite: Effect of Fabrication Parameters

Modeling and Grey Relational Multi-response Optimization of Chemical Additives and Engine Parameters on Performance Efficiency of Diesel Engine

Computational Analysis on the Selection of High Efficient Biodegradable-PolymerBased Nanofluids for Drilling Mud

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