Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

SmartMat, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 9, 2025

ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, models. They can target various definitions, distributions, correlations of concerned physical parameters given polymer systems, have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages building quantitative multivariate largely enhanced the capability scientific understanding discoveries, thus facilitating mechanism exploration, prediction, high‐throughput screening, optimization, rational inverse designs. This article summarizes representative progress recent two decades focusing on design, preparation, application, sustainable development materials based exploration key composition–process–structure–property–performance relationship. The integration both data‐driven insights through ML deepen fundamental discover novel is categorically presented. Despite construction application robust models, strategies algorithms deal with variant tasks science still rapid growth. challenges prospects then We believe that innovation will thrive along approaches, from efficient design applications.

Language: Английский

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Fabrication and multi-aspect characterization of polymer composite reinforced with differently stacked flax-fiber and steel-wire meshes

Journal of Elastomers & Plastics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

This study focuses on developing a new type of multi-fiber reinforced multi-layer polymer composite using natural and metal fibers. Flax fiber has shown potential as fairly good reinforcement, but its mechanical weaknesses have led researchers to explore hybridization in composites for better performance. The paper reports the fabrication characterization unique kind hybrid that uses advantages stainless steel along with flax fiber, resulting lightweight material suitable engineering uses. In this research, SS-304 wire mesh is used fibers make three different composites, varying stacking arrangements. Experimental results indicate that, although inclusion slightly reduces interlaminar shear strength, significant improvements are observed tensile flexural hardness impact resistance. Stereo-microscopy utilized analyze surface features sequences, while scanning electron microscopy (SEM) reveals fracture surfaces specimens. Energy-dispersive X-ray spectroscopy (EDX) mapping helps identify elemental compositions present composites. demonstrates reinforcement can enhance properties flax-reinforced providing sustainable solution applications.

Language: Английский

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Enhanced Mechanical Properties and Machinability of Al-Cu-SiC-GNP Smart Hybrid Composite Using Machine Learning Optimization

Arabian Journal for Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Language: Английский

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Parametric study of drilling process in ramie-epoxy composites using grey relational approach and artificial neural networks

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

Published: Feb. 5, 2025

Drilling is one of the primary and most preferred machining operations for producing holes in composite materials. Unlike conventional materials, drilling fiber reinforced polymer (FRP) more challenging because its heterogeneous anisotropic nature. In present study on behavior, ramie-epoxy composites are fabricated then subjected to tests following Taguchi L 16 orthogonal array. The influence spindle speed, feed rate drill diameter thrust force, torque delamination studied using technique. optimization parameters multi-response characteristics analyzed grey relational approach. results from analysis (GRA) indicate that dominant factor influencing multi-performance contributing 77.41% followed by speed with individual contributions 6.95% 5.51% respectively. It found optimal setting attaining minimum simultaneously can be obtained while at a 4000 rpm, 40 mm/min 4 mm Further, an artificial neural network (ANN) model proposed as tool effectively predict delamination. showed fairly good predicting high accuracies 89.593%, 95.144% 96.186% errors ±2%, ± 3% ±0.3% Analysis chip formation mechanisms morphological drilled done type chips formed during identify possible causes

Language: Английский

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Multi-Criteria and CNN Analysis of Al2O3/ TiO2/ Egg Shell/ ATH Ceramic Fillers in Glass Fiber-epoxy composites

Journal of Materials Research and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Tribological behavior of PLA reinforced with boron nitride nanoparticles using Taguchi and Machine learning approaches

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104772 - 104772

Published: April 1, 2025

Language: Английский

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Optimizing bio-hybrid composites for impact resistance using machine learning

Journal of the Brazilian Society of Mechanical Sciences and Engineering, Journal Year: 2025, Volume and Issue: 47(5)

Published: April 4, 2025

Language: Английский

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Parametric appraisal and wear prediction of hybrid composites using an integrated soft computing approach

Polymer Composites, Journal Year: 2024, Volume and Issue: 45(11), P. 10155 - 10171

Published: April 23, 2024

Abstract This article reports on the implementation of artificial neural network (ANN) and statistical methods to analyze predict erosion performance titania (TiO 2 ) filled ramie‐epoxy based composites. These hybrid composites are prepared by conventional hand lay‐up route subjected solid particle tests as per Taguchi's L 27 orthogonal array. The effects different control factors rate these studied using Taguchi method. It reveals that impact velocity filler content have significant effect wear followed other least factors. individual each factor while keeping constant is ascertained performing steady state experiments. Further, a computational model ANN used tool effectively rates results show predicted values in reasonably good agreement with experimental ones an accuracy 90% relative error lying within range 1%–10%. trained validated considering obtained during process input parameter. possible mechanisms causing loss identified electron microscopy. Highlights Successful fabrication titanium oxide reinforced Steady experiments performed. Erosion compared data. Wear

Language: Английский

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Sliding wear characteristics of epoxy composites reinforced with steel wire and flax fiber mats: An experimental and analytical study

Polymer Composites, Journal Year: 2024, Volume and Issue: 45(18), P. 17222 - 17238

Published: Aug. 23, 2024

Abstract This study evaluates the sliding wear behavior of hybrid composites made epoxy reinforced with alternating layers flax fiber mats and steel wire mesh. The aim is to enhance resistance fiber‐epoxy by incorporating Specifically, three composite types are produced varying their stacking sequences using simple hand layup technique. Dry tests performed on a pin‐on‐disc test rig under different conditions according ASTM G99 standard. Taguchi analysis an L 25 orthogonal array it reveals that velocity most critical factor, followed normal load, in affecting rate steel‐flax‐epoxy composite. Subsequently, steady‐state shows load increase specific (SWR), while reinforcement decreases it. ANOVA results indicate significantly impacts rates extent 69.47% for flax‐epoxy more than 70% flax‐steel‐epoxy composites. Additionally, electron microscopy used worn surfaces determine mechanisms. Highlights Successful fabrication flax‐steel mesh Wear multi‐layer sequence. Use statistical technique prediction rate. mechanisms identified microscopy.

Language: Английский

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A comparison of fabrication routes and property evaluation methods for bio‐particulate filled glass‐epoxy composites

Polymer Composites, Journal Year: 2024, Volume and Issue: 45(15), P. 14302 - 14317

Published: July 10, 2024

Abstract This research work aims to investigate the effect of fabrication techniques on physico‐mechanical properties pistachio shell (PS) filled glass‐epoxy composites and compare experimental findings with numerical simulation results using finite element analysis (FEM). These hybrid are fabricated by vacuum‐assisted resin transfer molding (VARTM) hand layup (HL) fixed glass fiber fraction (20 wt. %) but different PS powder loadings 0–30 %. The then characterized for physical, compositional, micro‐structural mechanical properties. filler is performed electron microscopy stereo‐microscopy. To identify phases present in both raw composite, an x‐ray diffraction test performed. presence functional groups identified help Fourier transform infrared spectroscopy (FTIR). It observed that there a reasonable improvement increase content, irrespective process used. density void also greatly affected amount particles composite technique. FEM carried out ANSYS 22.0 workbench determine characteristic numerically. comparison yields obtained VARTM close ones errors lying range 1%–4%. can possibly find potential applications light duty structures wear resistant applications. Highlights Development HL techniques. Physical alter loading. Evaluation analysis. Validating model comparing results.

Language: Английский

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