Machinability investigation of natural fibers reinforced polymer matrix composite under drilling: Leveraging machine learning in bioengineering applications

AIP Advances, Journal Year: 2024, Volume and Issue: 14(4)

Published: April 1, 2024

The growing demand for fiber-reinforced polymer (FRP) in industrial applications has prompted the exploration of natural fiber-based composites as a viable alternative to synthetic fibers. Using jute–rattan composite offers potential environmentally sustainable waste material decomposition and cost reduction compared conventional fiber materials. This article focuses on impact different machining constraints surface roughness delamination during drilling process FRP composite. Inspired by this unexplored research area, emphasizes influence various Response methodology designs experiment using drill bit material, spindle speed, feed rate input variables measure factors. technique order preference similarity ideal solution method is used optimize parameters, predicting delamination, two machine learning-based models named random forest (RF) support vector (SVM) are utilized. To evaluate accuracy predicted values, correlation coefficient (R2), mean absolute percentage error, squared error were used. RF performed better comparison with SVM, higher value R2 both testing training datasets, which 0.997, 0.981, 0.985 roughness, entry exit respectively. Hence, study presents an innovative through learning techniques.

Language: Английский

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Nanoparticle-enhanced biodiesel blends: A comprehensive review on improving engine performance and emissions

Materials Science for Energy Technologies, Journal Year: 2024, Volume and Issue: 7, P. 257 - 273

Published: Jan. 1, 2024

Environmental sustainability concerns have led to exploring alternative fuels like biodiesel in transportation. However, engines emit pollutants NOx, CO, and PM, posing health environmental risks. This review explores the use of Aluminium Oxide (Al2O3), Ruthenium (RuO2), Titanium (TiO2), Cerium (CeO2), Graphene Oxide, Multi-walled Carbon Nanotubes (CNT) other nanoparticles, engine. It focuses on their unique properties, characterization, emission control, impact, engine performance. The study emphasizes significance different blends, compositions, nanoparticle additions determining performance emissions. Results vary based type, size, concentration, blend composition. examines impact nanoparticles various aspects including density, viscosity, cetane number, calorific value, flash points. found that additives significantly influence Brake Thermal Efficiency combustion efficiency. also nanoparticle-enhanced blends improved ignition faster evaporation, higher oxygen content, elevated numbers, leading cleaner more environmentally friendly operation. research supports beneficial effects characteristics emissions reduction. suggests can improve fuel characteristics, performance, reduction but cautions against potential findings suggest further optimization for sustainable efficient pursuing greener transportation fuels, highlighting blends.

Language: Английский

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Homogeneity, metallurgical, mechanical, wear, and corrosion behavior of Ni and B4C coatings deposited on 304 stainless steels developed by microwave cladding technique

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 27, P. 5854 - 5867

Published: Nov. 1, 2023

The microwave cladding technique for depositing Ni and 10 % B4C coatings on 304 stainless steel has yielded significant advancements in material properties performance. key findings of this study revealed remarkable improvements, including a 43.33% increase hardness, indicating enhanced wear resistance mechanical properties. This improvement was attributed to the uniform distribution surface, ensuring consistent interfacial layer developed between SS surface without cracks porosity. Microstructural analysis at 500× magnification unveiled an impressive 2233.35 grains per square inch, showcasing refined grain structure achieved during process. Wear testing demonstrated low rate 0.00308 mm³/m favorable coefficient friction 0.1981, confirming material's suitability applications with demanding frictional conditions. Furthermore, corrosion behavior coated assessed, revealing minimal weight loss only 0.42 mg 10% sample. presence various carbide phases, such as Cr2C, Cr23C6, Cr7BC4, Fe5C2, Fe23B6, within further contributed

Language: Английский

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Enhancement in wear-resistance of 30MNCRB5 boron steel-substrate using HVOF thermal sprayed WC–10%Co–4%Cr coatings: a comprehensive research on microstructural, tribological, and morphological analysis

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 27, P. 1072 - 1096

Published: Oct. 5, 2023

The rotavator blade, a part of an agricultural equipment rotavator, is used for the soil bed preparation. These blades have direct interaction with in land. Bad, rocky, gravel, sandy, and high rough-hard texture are main factor to damage surface blade wear rate observed. This causes decrease overall life blade. It also changes geometry after few operations, that all effects performance capability this Hence HVOF (High-velocity oxy-fuel) modify improved resistance blade’s surface. Feedstock powder coating WC-10%Co-4%Cr Ni-20%Cr as bond coat on 30MNCRB5 steel substrate (Rotavator material). Six samples prepared test it Pin-On-Disc testing apparatus determine rate, weight loss, cumulative volume loss linear three bare coated 8mm diameter 30mm length cylindrical pin shape. Coated characterized using XRD (X-ray diffraction), SEM (Scanning electron microscope) EDAX (Energy-dispersive spectroscopy), X-ray mapping techniques. Worn out surfaces investigated study microstructure worn helped identify behavior. spray drastically defend from wear, very less was seen compared material. Weight determined by (30MNCRB5) material at 40N, 50N, 60N loads 2.996 ×10-3 Kgm, 3.003 3.123 (WC-10%Co-4%Cr) sample 0.006 0.030 0.038 Kgm respectively.

Language: Английский

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Tribological properties of CNT-filled epoxy-carbon fabric composites: Optimization and modelling by machine learning

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 28, P. 2582 - 2601

Published: Dec. 24, 2023

Polymer matrix composites reinforced with fibers/fillers are extensively used in several tribological components of automotive and boating applications. The mechanical performance polymer improves by incorporating nanofillers as secondary reinforcement. present research work fabricated carbon fabric-reinforced epoxy using the hand layup. were 0.1 wt%, 0.2 0.5 wt% nanotubes (CNT) fillers Tribological properties filled CNT have been carried out a pin‐on‐disc method. Adding significantly behaviour reducing wear rate coefficient friction. large surface area interaction due to higher aspect ratio shows improved adhesion between fabrics. It various characteristics composites—also, an analysis worn surfaces is analyze mechanisms scanning electronic microscopy. employs combination experimental analyses machine learning (ML) techniques explore resistance, hardness, predictive modeling volume loss composites. hyperparameter fine-tuning ML algorithms, including Random Forest (RF), k-Nearest Neighbors (KNN), XGBoost, demonstrates superior capabilities, particularly RF. study bridges material science, ML, practical applications, contributing valuable insights for developing advanced composite materials.

Language: Английский

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Unveiling of grain structure, porosity, phase distributions, microstructural morphology, surface hardness, and tribo-corrosion characteristics of nickel, and titanium dioxide-based SS-304 steel microwave composite coatings cladding

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 28, P. 4299 - 4316

Published: Dec. 30, 2023

The microstructure, homogeneity, and tribo-corrosion behavior of microwave-developed Nickel as well titanium dioxide SS-304 cladding surfaces are the primary emphasis this research. study includes assessing hardness enhancement from Ni TiO2 particles in surface. investigation additionally evaluates surface wear rates, friction coefficients, resistance to corrosion under tribological conditions. with developed by microwave energy were investigated study. microstructure was being examined validate uniformly homogeneous dispersion particles, XRD employed determine phases. evaluated, a pin-on-disk tribometer has assessed behavior. Tribo-corrosion tested 3.5-percent NaCl solution. To enhance cladding's efficiency, hybrid heating (MHH) utilising charcoal susceptor been employed. Findings exhibited that analysis showed had uniform distribution compact microstructure. significantly improved about 37.68% due incorporation 10% particles. FeNi3, NiSi2, Ni3C, NiC, Ni2Si, FeNi, phases seen on conditions also evaluated using tribometer. outcomes have decreased rates coefficients compared uncoated substrate. Moreover, 3.5% rate coefficient measured be 0.00412 mm3/m 0.297, respectively. results indicated enhanced

Language: Английский

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Enhancing tribo-mechanical, microstructural morphology, and corrosion performance of AZ91D-magnesium composites through the synergistic reinforcements of silicon nitride and waste glass powder

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 8, 2024

Abstract The present investigation has employed recycled waste glass powder (WGP) and silicon nitride (Si 3 N 4 ) as reinforcing-agents within AZ91D-matrix composites. composites were fabricated by employing the vacuum stir casting technique to mitigate effects of oxidation ensure homogeneity, uniformity, superior wettability among reinforcements. A microscopic study provided confirmation a uniform dispersion WGP Si particles throughout AZ91D-matrix. tensile strength AZ91D/WGP/Si rise with inclusion particulates up 1.5 percent in AZ91D/7.5% . However, AZ91D/9%Si composite have showed maximum value compared other chosen formulations/combinations current investigation. AZ91D/1.5% WGP/7.5% strengthened 12.13 comparison base alloy In A1 formulated composite, amount particulate enhanced hardness AZ91D-alloy percent. Findings, nevertheless exhibited that A6 had outcomes terms hardness. incorporation “reinforcing-constituent particulates” 1.5%WGP + 7.5%Si combination AZ91D-matrix, further increased fatigue-strength around 57.84 weight-loss 0.312 mg was being unveiled for composite. however, reported be 0.294 mg. At 5 loads, 2 m/s sliding speed, 1000 m distance, developed 1.5%WGP/7.5%Si /AZ91D rate wear, frictional coefficient 0.0025 mm /m 0.315, respectively. scanning electron microscopy (SEM) identified presence corrosion pits on surfaces undergone corrosion. These found result localised surface assaults occurring corrosive environments. Additionally, SEM pictures worn indicated emergence microcracks, which may associated conditions cyclic loading. Moreover, tensile-fractography examination brittle fracture failure, including cracks debonding phenomena. addition, EDS spectra-analysis revealed an apparent existence observed Mg-peak, Si-peak, Al-peak, Ca-peak, O-peak Furthermore, utilisation X-ray diffraction analysis effectively determined hard phases inside significantly contributed enhancement wear resistance. development harder-phases included, α-Mg, Al 12 Mg 17, SiO , MgO, CaO been accountable tribomechanical, wear-resistance characteristics discovered substantial impact enhancing mechanical performance raising resistance wear.

Language: Английский

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Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Journal of Sustainability for Energy, Journal Year: 2024, Volume and Issue: 3(1), P. 1 - 20

Published: Feb. 4, 2024

The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency systems. This article conducts an exhaustive analysis critical aspects concerning AI-sustainable nexus, encompassing challenges in technological facilitation intelligent decision-making processes pivotal for frameworks. It is demonstrated that AI applications, ranging from optimization algorithms predictive analytics, possess revolutionary capacity bolster energy. However, this not without its challenges, which span complexities socio-economic impacts. underscores imperative deploying manner transparent, equitable, inclusive. Best practices solutions are proposed navigate these effectively. Additionally, discourse extends recent advancements AI, including edge computing, quantum explainable offering insights evolving landscape Future research directions delineated, emphasizing importance enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining ramifications, exploring models human-AI collaboration, fortifying security measures, evaluating impact emerging technologies. comprehensive aims inform academics, practitioners, policymakers, guiding creation resilient future.

Language: Английский

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Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

ACS Omega, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 5, 2024

Recent studies focus on enhancing the mechanical features of natural fiber composites to replace synthetic fibers that are highly useful in building, automotive, and packing industries. The novelty work is woven areca sheath (ASF) with different fraction epoxy has been fabricated tested for its tribological responses three-body abrasion wear testing machines along features. impact various examined. study also revolves around development validation a machine learning predictive model using random forest (RF) algorithm, aimed at forecasting two critical performance parameters: specific rate (SWR) coefficient friction (COF). void observed vary between 0.261 3.8% as incremented. hardness mat rises progressively from 40.23 84.26 HRB. A fair ascent tensile strength modulus observed. Even though short descent flexural seen 0 12 wt % composite specimens, they incrementally raised finest values 52.84 2860 MPa, respectively, pertinent 48 fiber-loaded specimen. progressive rise ILSS perceptible. behavior specimens reported. worn surface morphology studied understand interface ASF matrix. RF exhibited outstanding prowess, evidenced by high R-squared coupled low mean-square error mean absolute metrics. Rigorous statistical employing paired t tests confirmed model's suitability, revealing no significant disparities predicted actual both SWR COF.

Language: Английский

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Enhancing high-speed EDM performance of hybrid aluminium matrix composite by genetic algorithm integrated neural network optimization

Journal of Materials Research and Technology, Journal Year: 2024, Volume and Issue: 31, P. 4113 - 4127

Published: July 1, 2024

Hybrid aluminium matrix composites (HAMCs) are highly valued in manufacturing sectors but difficult to machine conventionally due reinforcements' inherent hardness and abrasiveness. This research finds high-speed wire electric discharge machining (WEDM) be a potent solution for stir-squeeze-casted HAMC (AA2024 with ceramic nanoparticles: Al2O3, SiC, Si3N4, BN) creating complex profiles superior erosion characteristics. The performance has been assessed terms of material removal rate (MRR) different (plane, angular, curve), wear ratio (WWR) by employing variables, i.e., pulse voltage (PV), current (PI), feed (WFR), (P), drum speed (DS). Results revealed that MRR_curve highest MRR (37.84 mm3/min), followed MRR_angular (36.07 mm3/min) MRR_plane (34.40 mm3/min). lowest WWR (0.0094%) was achieved at lower magnitudes variables. microscopic observations unveil shallow craters, minute-sized melt redeposits, micro-pores on machined surface under the conditions PV = 90 V, PI 2 A, WFR 13 m/min, P 20 mu, DS 40 Hz. Optimization using non-dominated-sorting-genetic algorithm (NSGA-II) resulted significant enhancements 75.37, 73.90, 76.01% MRR_plane, MRR_angular, MRR_curve, respectively, depreciation 16.50% WWR.

Language: Английский

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