Biobased hybrid composite design for optimum hardness and wear resistance

Okafor, Christian Emeka; Sunday, Iweriolor; Ani, Okwuchukwu Innocent; Akçakale, Nürettin; Ekwueme, Godspower Onyekachukwu; Ugwu, Peter Chukwuemeka; Nwanna, Emmanuel Chukwudi; Onovo, Anthony Chinweuba

doi:10.1016/j.jcomc.2022.100338

Cited by 6 publications

(2 citation statements)

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“…The NaOH treatment, which is wellknown for changing the surface properties of fibers, is expected to have a considerable influence on the tribological characteristics of the composite. [7][8][9] Understanding and optimizing tribological performance require a systematic approach, and the design of experiments (DOE) serves as a powerful methodology for this purpose. 10 By employing a factorial design, this study investigates the effects of key parameters disc speed, wear duration, and NaOH treatment percentage on two critical tribological metrics: specific wear rate (SWR) and coefficient of friction (COF).…”

Section: Introductionmentioning

confidence: 99%

“…The emphasis is on understanding the delicate interplay between critical parameters such as disc speed, wear duration, and NaOH treatment percentage. The NaOH treatment, which is well‐known for changing the surface properties of fibers, is expected to have a considerable influence on the tribological characteristics of the composite 7–9 …”

Section: Introductionmentioning

confidence: 99%

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Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio‐composite materials

Sengottaiyan,

Subbarayan,

Viswanathan

et al. 2024

Polymer Composites

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This study investigates the influence of NaOH treatment on tribological behavior in hybrid fiber‐reinforced composites, specifically employing Banana fiber with Al2O3 filler in an epoxy matrix. Through design of experiments (DOE), disc speed, wear duration, and NaOH treatment are analyzed for specific wear rate (SWR) and coefficient of friction (COF). To advance the understanding of wear characteristics, the study leverages advanced machine learning, using Python‐powered artificial neural networks (ANN), is integrated with innovative ANN hyperparameter optimization. Optimized parameters (1050 rpm, 60 s, 5% treatment) significantly minimize SWR (12.38 × 10−5 mm3/Nm) and COF (0.2). Scanning electron microscopy (SEM) analysis reveals improved interfacial adhesion and identifies micro‐cracks as the primary wear mechanism. This work contributes to a profound understanding of wear in hybrid composites, offering a fine‐tuned predictive model for optimizing tribological behavior and advancing material science and engineering.Highlights Reduced SWR, COF in composites via NaOH optimization. DOE: Explored disc speed, duration, NaOH impact. Advanced machine learning techniques enhanced wear prediction. Innovative: ANN hyperparameter optimization. Optimal Parameters: 1050 rpm, 60 s, 5% treatment.

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