Influence of treatment and fly ash fillers on the mechanical and tribological properties of banana fiber epoxy composites: experimental and ANN-RSM modeling

Composite Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 33

Published: Jan. 6, 2025

This research examines the impact of chemical treatment and banana fly ash fillers on mechanical, tribological, water absorption characteristics fiber-reinforced epoxy composites. Alkaline enhanced fiber-matrix adhesion, markedly improving mechanical characteristics. The optimal performance occurred at 10% content, yielding a tensile strength 40.25 MPa, flexural 77.23 an 44.82 kJ/m2. Water studies indicated decline in moisture uptake, reducing from 40% untreated composites to 25% containing 15% ash, due bonding fewer voids. Tribological experiments demonstrated decrease Specific Wear Rate (SWR) Coefficient Friction (COF) with elevated concentration, signifying improved wear resistance. Predictive modeling Artificial Neural Networks (ANN) showed accuracy (mean error: 0.9584% for SWR, 0.50265% COF). RSM optimization identified input parameters minimizing SWR COF: sliding velocity 5.14491 m/s, distance 652.05 m, content 12.6236%, minimum COF values 15.63 × 10− 5 mm3/Nm 0.242241, respectively. SEM analysis confirmed that treated fibers minimized crack propagation while fracture toughness. results underscore promise ash-filled automotive, aerospace, structural applications necessitate moisture-resistant

Language: Английский

---

Enhancing mechanical, degradation, and tribological properties of biocomposites via treatment and alumina content

Journal of Reinforced Plastics and Composites, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

The objective of this study is to investigate the effects alumina filler content and NaOH-treated Roselle fibers on mechanical, thermal, biodegradation, tribological properties while identifying optimal conditions for eco-friendly applications. Compression molding was employed fabricate composites, results revealed significant improvements in performance with chemical treatment content. Mechanical testing showed that 10% composite exhibited highest tensile, flexural, impact strengths due enhanced interfacial bonding uniform dispersion. Thermal analysis demonstrated improved stability, offering best thermal degradation resistance. Biodegradation studies indicated slower weight loss alumina-filled highlighting their environmental durability. Tribological evaluations achieved lowest specific wear rate (SWR) coefficient friction (COF), supported by SEM showing minimal debris surface damage. Optimization using a simulated annealing algorithm identified ideal (sliding velocity: 6.6 m/s, sliding distance: 500.33 m, content: 10.62%) minimized SWR (13.28 × 10⁻⁵ mm³/Nm) COF (0.278). These findings provide valuable insights into fiber composites sustainable applications automotive packaging industries.

Language: Английский

---

Optimized Machine Learning Prediction and RSM Optimization of Mechanical Properties in Boiled Eggshell Filler-Added Biocomposites

Fibers and Polymers, Journal Year: 2024, Volume and Issue: 25(8), P. 3115 - 3133

Published: July 19, 2024

Language: Английский

---

Assessment of mechanical, thermal, and sliding wear performance of chemically treated alumina‐filled biocomposites using machine learning and response surface methodology

Polymer Composites, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Abstract This study examines how NaOH treatment and alumina filler affect the mechanical properties, water absorption, thermal degradation, sliding wear of epoxy composites reinforced with pineapple leaf fiber. greatly improved composites' tensile, flexural, impact strengths by strengthening bond between fiber matrix. Furthermore, incorporation further elevated properties. The composite 10% showed peak values 41.4 MPa in tensile strength, 63.8 flexural 37.6 kJ/m 2 strength. Because hygroscopic parts were removed from treated composites, they absorbed much less water. 15% had lowest absorption at 18% after 192 h. Thermal degradation analysis that stability, having highest char residue (15.3%) 700°C. Sliding tests reinforcement significantly reduced specific rate (SWR) coefficient friction (COF). an SWR 0.2598 × 10 −5 mm 3 /Nm a COF 0.103 when 120 cm/s, 45 N load over 1500 m distance. A scanning electron microscopy found untreated experienced severe abrasive wear, while exhibited mild adhesive wear. shows treating PALF adding enhance their mechanical, thermal, tribological making them suitable for high‐performance industrial applications. Highlights Alumina (41.4 MPa) strength (63.8 MPa). NaOH‐treated moisture, enhancing durability. stability improved, 15.3% 700°C alumina. Optimized achieved (0.2598 /Nm). Artificial neural network response surface methodology accurately predicted optimized behavior.

Language: Английский

---

ADVANCED ENSEMBLE MACHINE LEARNING PREDICTION TO ENHANCE THE ACCURACY OF ABRASIVE WATERJET MACHINING FOR BIOCOMPOSITES

Materials Chemistry and Physics, Journal Year: 2024, Volume and Issue: unknown, P. 130175 - 130175

Published: Nov. 1, 2024

Language: Английский

---

Ensemble machine learning for predicting and enhancing tribological performance of Al5083 alloy with HEA reinforcement

Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

This study investigates the tribological behavior of Al5083 alloy reinforced with AlCoCrFeNiSi high-entropy (HEA) particles using friction stir processing (FSP). Wear characteristics were analyzed pin-on-disc experiments across varying HEA volume percentages, disc speeds, and test durations, revealing significant improvements in wear resistance increasing content. Machine learning techniques, including artificial neural networks (ANN) long short-term memory (LSTM) networks, employed to predict specific rate (SWR) coefficient (COF) high accuracy. The ensemble model combining ANN LSTM architectures achieved R-squared values 0.9653 for SWR 0.9718 COF, a root mean square error (RMSE) 0.024 0.017 COF respectively, indicating robust predictive capabilities. Cross-validation further validated model's effectiveness, achieving an average prediction 2.13% 1.89% COF. Response surface methodology (RSM) optimization refined process parameter relationships, identifying conditions that minimize 3.57 × 10 – 6 mm³/Nm 0.237. Scanning electron microscopy (SEM) analysis worn surfaces confirmed effectiveness reinforcement mitigating mechanisms, enhancing material's durability by 45% compared unreinforced alloy. comprehensive approach advances understanding HEA-reinforced composites. It provides practical insights optimizing material performance industrial applications, contributing developing high-performance materials tailored resistance.

Language: Английский

---

Optimization and Finite Element Analysis Simulation on Mechanical Behavior of Wire Arc Additive Manufacturing for SS316L Using Response Surface Methodology

Journal of Materials Engineering and Performance, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Language: Английский

---

Machine Learning Prediction and Optimization of Cold Metal Transfer Welding Parameters for Enhancing the Mechanical and Microstructural Properties of Austenitic-Ferritic Stainless-Steel Joints

Journal of Materials Engineering and Performance, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Language: Английский

---

Sustainable Composites from Sugarcane Bagasse Fibers and Bio-Based Epoxy with Insights into Wear Performance, Thermal Stability, and Machine Learning Predictive Modeling

Journal of Composites Science, Journal Year: 2025, Volume and Issue: 9(3), P. 124 - 124

Published: March 6, 2025

The global push for sustainable materials has intensified the research on natural fiber-reinforced composites. This study investigates potential of sugarcane bagasse fibers, combined with a bio-based epoxy matrix, as alternative high-performance A comprehensive approach was adopted, including wear testing, thermal and structural characterization, machine learning predictive modeling. Ethylene dichloride-treated fibers exhibited lowest rate (0.245 mg/m) highest stability (T20% = 260 °C, char yield 1.3 mg), highlighting role optimized surface modifications. XRD (X-ray diffraction) analysis revealed that pre-treated achieved crystallinity index 62%, underscoring importance alignment in fiber-matrix bonding. Machine insights using Random Forest model identified fiber treatment most significant parameter influencing performance, accurate predictions validated through experimental results. work demonstrates transformative polymer composites, offering pathway environmentally friendly, lightweight, durable material solutions. These findings integrate rigor computational insights, paving way advancements fiber-based composite technologies.

Language: Английский

---

Advanced ensemble machine learning and response surface methodology for optimizing and predicting tribological performance of CMT-WAAM fabricated Al5356 alloy

International Journal on Interactive Design and Manufacturing (IJIDeM), Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Language: Английский

---