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

Journal of Reinforced Plastics and Composites, Год журнала: 2025, Номер unknown

Опубликована: Фев. 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.

Язык: Английский

---

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

Fibers and Polymers, Год журнала: 2024, Номер 25(8), С. 3115 - 3133

Опубликована: Июль 19, 2024

Язык: Английский

---

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

Polymer Composites, Год журнала: 2025, Номер unknown

Опубликована: Март 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.

Язык: Английский

---

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

Materials Chemistry and Physics, Год журнала: 2024, Номер unknown, С. 130175 - 130175

Опубликована: Ноя. 1, 2024

Язык: Английский

---

Supervised Machine learning models for predicting mechanical properties of dissimilar friction stir welded AA7075-AA5083 aluminum alloys

Measurement, Год журнала: 2025, Номер unknown, С. 116653 - 116653

Опубликована: Янв. 1, 2025

Язык: 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, Год журнала: 2025, Номер unknown

Опубликована: Янв. 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.

Язык: 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, Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

Язык: 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, Год журнала: 2025, Номер unknown

Опубликована: Фев. 14, 2025

Язык: Английский

---

Optimization and Comparative Analysis of Machining Performance of Al–Cu–SiC–GNP Composite: Influence of Reinforcement Variations Using Machine Learning, RSM, and ANOVA Validation

Advanced Engineering Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 14, 2025

This study aims to optimize and analyze the machinability of Al–Cu–SiC–GNP composites using advanced techniques such as machine learning, (RSM), (ANOVA). The are fabricated an ex situ stir casting process with varying reinforcement percentages silicon carbide (SiC) graphene nanoplatelets (GNP) (2, 3, 5%), their is evaluated during water jet machining. key parameters analyzed material removal rate, surface roughness ( R a ), kerf width. Experimental findings reveal that significantly influence machinability. Optimal results achieved 5% SiC, 3% GNP, 300 MPa, 120 mm min −1 , balancing enhanced mechanical properties efficient ML models, including decision tree, random forest, support vector machine, artificial neural network (ANN), applied predict machining outcomes. Among these, ANN model exhibits highest predictive accuracy, capturing complex nonlinear interactions between input parameters. also validates through RSM ANOVA, confirming statistical significance on research provides robust framework for optimizing hybrid composite offers valuable insights into relationship content, parameters, performance outcomes, making it highly applicable aerospace automotive.

Язык: Английский

---

Comparison of machine learning algorithms for dynamic performance assessment in complex shapes manufacturing of hybrid particle-reinforced composite

Expert Systems with Applications, Год журнала: 2025, Номер 275, С. 127022 - 127022

Опубликована: Март 3, 2025

Язык: Английский

---