Optimization of process variables for tensile properties of bagasse fiber-reinforced composites using response surface methodology

Siddique, Sheraz Hussain; Faisal, Saira; Ali, Muhammad; Gong, Rong

doi:10.1177/0967391120968432

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…The percentage errors between the actual and predicted values were 1.12%, 1.87%, 1.39%, and 2.72% for PLA/clay composites loaded with NaOH-modified K85 rice husks, Mg(OH) 2 -modified K85 rice husks, NaOH-modified K98 rice husks, and Mg(OH) 2 -modified K98 rice husks respectively. This suggested that the models' adequacies were reasonable and hence validity was confirmed [35,46,63]. Additionally, the results obtained in each set of composites…”

Section: Process Optimizationsupporting

confidence: 54%

Optimization of tensile strength of PLA/clay/rice husk composites using Box-Behnken design

Yiga

Lubwama

Pagel

et al. 2021

Biomass Conv. Bioref.

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It is extremely important to save costs and time while enhancing accuracy in experimentation. However, no study has utilized response surface methodology (RSM) to obtain the effects of independent parameters on properties of PLA/clay/rice husk composites. This study focused on optimization of tensile strength of fiber-reinforced polylactic acid (PLA) composites. RSM using Box-Behnken design (BBD) was used to determine optimum blending parameters of the developed composites. Fiber-reinforced PLA composites were prepared using compression molding. Rice husk fiber and clay filler were used to enhance tensile properties of PLA. Five factors, namely, clay filler loading (1 − 5 wt.%), rice husk fiber loading (10 − 30 wt.%), alkali concentration (0 − 4 wt.%), rice husk variety (K85, K98), and alkali type (NaOH, Mg(OH)2) were varied with 68 individual experiments. Tensile tests were carried out according to ASTM D638 standards. ANOVA results revealed that the quadratic models best fit the tensile strength response, with filler loading and fiber loading factors as the most significant model terms. Interaction effects were more predominant than linear and quadratic effects. The developed models used to determine maximum tensile strengths of PLA/clay/rice husk composites were in close agreement with experimental findings (R2 values of 0.9635, 0.9624, 0.9789, and 0.9731 for NaOH-modified K85 rice husks, Mg(OH)2-modified K85 rice husks, NaOH-modified K98 rice husks, and Mg(OH)2-modified K98 rice husks respectively). Individual optimal conditions were used to predict maximum tensile strengths in each set of developed composites. The predicted tensile strengths were 32.09 MPa, 33.69 MPa, 32.47 MPa, and 32.75 MPa for PLA/clay composites loaded with NaOH-modified K85 rice husks, Mg(OH)2-modified K85 rice husks, NaOH-modified K98 rice husks, and Mg(OH)2-modified K98 rice husks, respectively, which were very close to the obtained experimental values of 31.73 MPa, 33.06 MPa, 32.02 MPa, and 31.86 MPa respectively.

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Section: Process Optimizationsupporting

confidence: 54%

Optimization of tensile strength of PLA/clay/rice husk composites using Box-Behnken design

Yiga

Lubwama

Pagel

et al. 2021

Biomass Conv. Bioref.

View full text Add to dashboard Cite

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“…. , t j n (5) where m denotes the quantity of interpolation points (here, m = k + 1), n describes the number of dimensions, f t j 1 , t j 2 , . .…”

Section: Chebyshev Polynomial Fittingmentioning

confidence: 99%

“…Ghasemi et al [ 3 , 4 ] applied the RSM to predict and optimize the tensile strength of polypropylene-based nanocomposites. Siddique et al [ 5 ] examined the impact of process variables on the tensile properties of fiber-reinforced composites using the RSM. The Box–Behnken design in the RSM is used as a DOE method to conduct the modeling process.…”

Section: Introductionmentioning

confidence: 99%

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A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Li,

Zhu

2024

Polymers

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A novel method is proposed to quickly predict the tensile strength of carbon/epoxy composites with resin-missing defects. The univariate Chebyshev prediction model (UCPM) was developed using the dimension reduction method and Chebyshev polynomials. To enhance the computational efficiency and reduce the manual modeling workload, a parameterization script for the finite element model was established using Python during the model construction process. To validate the model, specimens with different defect sizes were prepared using the vacuum assistant resin infusion (VARI) process, the mechanical properties of the specimens were tested, and the model predictions were analyzed in comparison with the experimental results. Additionally, the impact of the order (second–ninth) on the predictive accuracy of the UCPM was examined, and the performance of the model was evaluated using statistical errors. The results demonstrate that the prediction model has a high prediction accuracy, with a maximum prediction error of 5.20% compared to the experimental results. A low order resulted in underfitting, while increasing the order can improve the prediction accuracy of the UCPM. However, if the order is too high, overfitting may occur, leading to a decrease in the prediction accuracy.

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Optimization of adsorbent dose and contact time for the production of jackfruit waste nutrient-enriched biochar

Nsubuga

Kabenge

Zziwa

et al. 2023

Waste Dispos. Sustain. Energy

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Optimization of process variables for tensile properties of bagasse fiber-reinforced composites using response surface methodology

Cited by 3 publications

References 27 publications

Optimization of tensile strength of PLA/clay/rice husk composites using Box-Behnken design

Optimization of tensile strength of PLA/clay/rice husk composites using Box-Behnken design

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Optimization of adsorbent dose and contact time for the production of jackfruit waste nutrient-enriched biochar

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