Predictive analysis of tensile strength ratios in laminated bamboo composites: Unraveling the stochastic impact of ply angle variations through machine learning model DOI
Deepak Kumar, Apurba Mandal

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 23, 2024

Laminated bamboo composites (LBC), made by sandwiching strips, offer promising alternatives to traditional construction materials, especially for housing. However, subjection the continuous static loading makes these materials initiate cracks inside their various ply. This study uses classical laminate theory (CLT) determine strength ratio (SR) of LBC at different ply orientations applying Tsai-Wu and Tsai-Hill failure criteria using MATLAB. The aims calculate SRs LBCs CLT, employing an ANN model stochastic finite element (FE) modeling investigate five-layered with varying orientations. Applying highest SR was determined be 1.5375 × 10 7 N/m [0°/0°/0°/0°/0°] laminate, as per both theories. reveals substantial variations depending on orientation, consistently showing higher predictions compared theory. Next, emphasizes deterministic methodologies account effects angle obtained LBCs. Monte Carlo simulation (MCS) utilized 10,000 randomly generated inputs associated SRs. By incorporating MCS introduce ±1% in angles utilizing normally distributed data, this research effectively captures uncertainties orientation. Finally, forecast random orientations, artificial neural network (ANN) surrogate is employed. analysis confirms need quantify uncertainties. findings are crucial advancing application sustainable construction, providing valuable insights into mechanical behavior under considering effect properties.

Language: Английский

Enhancing machining accuracy of banana fiber-reinforced composites with ensemble machine learning DOI

S. Saravanakumar,

S. Sathiyamurthy,

V. Vinoth

et al.

Measurement, Journal Year: 2024, Volume and Issue: 235, P. 114912 - 114912

Published: May 14, 2024

Language: Английский

Citations

24
Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites DOI

V. Vinoth,

S. Sathiyamurthy,

S. Saravanakumar

et al.

Polymer Composites, Journal Year: 2024, Volume and Issue: 45(7), P. 6077 - 6092

Published: Feb. 1, 2024

Abstract The aim of this investigation was to delve into the impact abrasive water jet machining (AWJM) process variables on surface roughness ( R a ) and kerf angle K hybrid fiber‐reinforced polyester composites. Utilizing both response methodology (RSM) artificial neural network (ANN) prediction models, study sought optimize input parameters for machining, specifically in context paddy straw PALF‐reinforced targeted optimization included flow rate, traverse standoff distance during AWJM. identified an optimal combination AWJM that effectively meets practical requirements According RSM, suggested values are rate set at 300 g/min, speed 110 mm/min, 1 mm. ANN exhibited robust predictive capabilities, achieving high 2 scores 0.932 0.962 angle, respectively. To enhance performance minimize researchers conducted parameters. Subsequently, confirmation experiments were executed validate model fine‐tune application. Highlights Investigated value Used RSM models parameter biocomposite. Optimal parameters: AFR (300 g/min), TS (110 mm/min), SOD (1 mm). showed strong predictions: ). Confirmation validated applications.

Language: Английский

Citations

17
Optimized machine learning with hyperparameter tuning and response surface methodology for predicting tribological performance in bio‐composite materials DOI

S. Saravanakumar,

S. Sathiyamurthy,

Vinoth Viswanathan

et al.

Polymer Composites, Journal Year: 2024, Volume and Issue: 45(10), P. 9421 - 9439

Published: April 9, 2024

Abstract This study investigates the influence of NaOH treatment on tribological behavior in hybrid fiber‐reinforced composites, specifically employing Banana fiber with Al 2 O 3 filler an epoxy matrix. Through design experiments (DOE), disc speed, wear duration, and are analyzed for specific rate (SWR) coefficient friction (COF). To advance understanding characteristics, leverages advanced machine learning, using Python‐powered artificial neural networks (ANN), is integrated innovative ANN hyperparameter optimization. Optimized parameters (1050 rpm, 60 s, 5% treatment) significantly minimize SWR (12.38 × 10 −5 mm /Nm) COF (0.2). Scanning electron microscopy (SEM) analysis reveals improved interfacial adhesion identifies micro‐cracks as primary mechanism. work contributes to a profound offering fine‐tuned predictive model optimizing advancing material science engineering. Highlights Reduced SWR, composites via DOE: Explored impact. Advanced learning techniques enhanced prediction. Innovative: Optimal Parameters: 1050 treatment.

Language: Английский

Citations

17
Influence of treatment and fly ash fillers on the mechanical and tribological properties of banana fiber epoxy composites: experimental and ANN-RSM modeling DOI

S. Saravanakumar,

S. Sathiyamurthy,

G. Selvakumar

et al.

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

Citations

3
Enhancing mechanical, degradation, and tribological properties of biocomposites via treatment and alumina content DOI

S. Saravanakumar,

S. Sathiyamurthy,

Ravikumar Natarajan

et al.

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

Citations

2
Enhancing tribological performance of hybrid fiber-reinforced composites through machine learning and response surface methodology DOI

S. Sathiyamurthy,

S. Saravanakumar,

V. Vinoth

et al.

Journal of Reinforced Plastics and Composites, Journal Year: 2024, Volume and Issue: unknown

Published: May 27, 2024

This study delves into the significant effects of sodium hydroxide (NaOH) treatment on tribological properties hybrid fiber-reinforced composites, specifically focusing combination paddy straw (PS) and pineapple leaf (PALF) in a polyester matrix. By leveraging Artificial Neural Networks (ANNs) to predict Specific Wear Rate (SWR) Coefficient Friction (COF), research employs grid search approach for hyperparameter optimization. optimization process results an optimal ANN architecture with impressive accuracy, showcasing low mean absolute error high R-squared values 0.991 0.986 SWR COF predictions, respectively. Utilizing Design Experiments (DOE), systematically analyzes intricate interplay disc speed, wear duration, NaOH percentage, specific focus as pivotal metrics. The Analysis Variance (ANOVA) underscore substantial impact duration percentage characteristics. Additionally, quadratic regression models reveal nuanced correlations, highlighting sensitivity influence COF. outcome emphasizes efficacy these parameters achieving superior performance composites. Beyond contributing profound understanding characteristics, this work introduces innovative dimension through optimized modeling, ensuring more accurate fine-tuned predictive model.

Language: Английский

Citations

12
Optimized Machine Learning Prediction and RSM Optimization of Mechanical Properties in Boiled Eggshell Filler-Added Biocomposites DOI
Gopi Periyappillai,

S. Sathiyamurthy,

S. Saravanakumar

et al.

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

Published: July 19, 2024

Language: Английский

Citations

10
Optimization of nano-filler and silane treatment on mechanical performance of nanographene hybrid composites using RSM and ANN technique DOI

Solairaju Jothi Arunachalam,

R. Saravanan,

Thanikodi Sathish

et al.

Journal of Adhesion Science and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: Sept. 16, 2024

Language: Английский

Citations

10
Assessment of mechanical, thermal, and sliding wear performance of chemically treated alumina‐filled biocomposites using machine learning and response surface methodology DOI Creative Commons

V.S. Shaisundaram,

S. Saravanakumar,

Rajesh Mohan

et al.

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

Citations

1
Effect of Alumina on Epoxy Composites with Banana Fiber: Mechanical, Water Resistance and Degradation Property Analysis DOI

S. Saravanakumar,

S. Sathiyamurthy,

V. Vinoth

et al.

Fibers and Polymers, Journal Year: 2023, Volume and Issue: 25(1), P. 275 - 287

Published: Nov. 11, 2023

Language: Английский

Citations

18