Cited by Enhancing optimization and reducing machining time of freeform shapes through modeling, simulation, and Taguchi design of experiments with artificial neural networks

Effect of sugarcane bagasse and alumina reinforcements on physical, mechanical, and thermal characteristics of epoxy composites using artificial neural networks and response surface methodology DOI

G. R. Arpitha,

H. Mohit,

Madhu Puttegowda

и другие.

Biomass Conversion and Biorefinery, Год журнала: 2023, Номер 14(11), С. 12539 - 12557

Опубликована: Фев. 3, 2023

Язык: Английский

Процитировано

Magnesium based alloys for reinforcing biopolymer composites and coatings: A critical overview on biomedical materials DOI

Akarsh Verma, Shigenobu Ogata

Advanced Industrial and Engineering Polymer Research, Год журнала: 2023, Номер 6(4), С. 341 - 355

Опубликована: Янв. 19, 2023

Magnesium (Mg) & its alloys are favourable for orthopaedic cardiovascular medical device fabrication applications, but holds a natural ability to degrade biologically when put with aqueous solution of the substances and/or water-saturated tissue in context living organism. Mg nature corrode inside organism body is mainly attributed excessive rates corrosion Mg. Poor resistance possessed by decreases mechanical properties implants, and adds toxic effects on bone metabolism. A potential method increasing alloy without changing protective polymeric deposit coatings. Moreover, impart better biocompatible aspects based materials biopolymers have been used as composite constituent. This review such constituting biopolymers. Their resulting osteopromotive conjunction biocompatibility may help clinicians fix existing related issues.

Язык: Английский

Процитировано

A comprehensive review on natural fillers reinforced polymer composites using fused deposition modeling DOI

P. Ramesh,

H. Mohit,

Sanjay Mavinkere Rangappa

и другие.

Polymer Composites, Год журнала: 2023, Номер 44(7), С. 3715 - 3747

Опубликована: Апрель 26, 2023

Abstract Additive manufacturing (AM) is being used more widely because of the simplicity operating procedures. It decreases material consumption during part production and eliminates waste. Fused deposition modeling (FDM), a well‐known AM process, uses thermoplastic polymer as feedstock to manufacture end design. Pure thermoplastic‐based products are still utilized prototypes in several sectors due their lack strength durability. This problem has been solved by strengthening thermoplastics adding reinforcing element. Composites made polymers various constituents known components. article overviews natural reinforced composites FDM process. Mechanical Thermal properties presented based on filler percentage, this only considered composite filaments for

Язык: Английский

Процитировано

Integrating machine learning and response surface methodology for analyzing anisotropic mechanical properties of biocomposites DOI

S. Saravanakumar,

S. Sathiyamurthy,

P. Pathmanaban

и другие.

Composite Interfaces, Год журнала: 2023, Номер 31(1), С. 1 - 28

Опубликована: Сен. 20, 2023

ABSTRACTThis study enhances the anisotropic mechanical properties of banana fiber-epoxy composites by optimizing fiber loading, orientation, and treatment using Response Surface Methodology (RSM) Artificial Neural Network (ANN). RSM suggests optimal values: loading at 33 wt%, NaOH 6.8 orientation 15 degrees. This material has exceptional characteristics, including a maximum tensile strength (TLS) 31.72 MPa, flexural (FLS) 42.86 impact (IPS) 38.56 kJm-2. ANN effectively predicts strengths with high R2 scores 0.969, 0.984, 0.954 for tensile, flexural, strengths. Incorporating batch normalization dropout layers robustness. The concludes that significantly composite's anisotropy.KEYWORDS: ANNbiocompositesfiber orientationalkali treatmentanisotropic behaviormechanical propertiesresponse surface methodology AcknowledgementsThe authors would like to acknowledge scheme Innovation, Technology Development, Deployment (1819) Department Science (DST) - Delhi.Disclosure statementNo potential conflict interest was reported author(s).Author contributionAuthor 1: Corresponding AuthorAuthor 2: Research GuideAuthor 3: Machine Learning Prediction model developed.

Язык: Английский

Процитировано

Development of machine learning models for the prediction of erosion wear of hybrid composites DOI

Sourav Kumar Mahapatra,

Alok Satapathy

Polymer Composites, Год журнала: 2024, Номер 45(9), С. 7950 - 7966

Опубликована: Март 14, 2024

Abstract This article reports on development of an adaptive framework for predicting the erosion performance polymer composites using certain statistical and machine learning (ML) models. For this, ramie‐epoxy reinforced with variations (0–30 wt%) sponge iron slag (an industry waste) are considered. The fabricated then subjected to high temperature solid particle wear trials following Taguchi's L 27 orthogonal array. effects different control factors rate in interactive environment appraised by analysis variance (ANOVA) which reveals filler content as most significant factor contributing 66.21%, followed impact velocity (22.86%) impingement angle (2.28%). A regression model based input–output parameters obtained from experimentation is constructed prediction rate. Further, four predictive models algorithms also proposed predict composites. feasibility each ML assessed appropriate metrics. Among all models, gradient boosting found be reliable exhibiting highest accuracy least errors. Highlights Development novel class slag. Database creation Data‐driven modeling rates learning. Comparison identifying best one.

Язык: Английский

Процитировано

Enhancing Mechanical and Tribological Properties of Hybrid Kenaf–Carbon Fiber Vinyl Ester Composites for Advanced Applications DOI

V Mahesh Kumar, Madhu Puttegowda,

Ballupete Nagaraju Sharath

и другие.

Journal of Materials Engineering and Performance, Год журнала: 2025, Номер unknown

Опубликована: Март 13, 2025

Язык: Английский

Процитировано

Basics of Density Functional Theory, Molecular Dynamics, and Monte Carlo Simulation Techniques in Materials Science DOI

Sandeep Kumar Singh, Ankur Chaurasia, Akarsh Verma

и другие.

Materials horizons, Год журнала: 2023, Номер unknown, С. 111 - 124

Опубликована: Янв. 1, 2023

Язык: Английский

Процитировано

Optimizing sustainable machining processes: a comparative study of multi-objective optimization techniques for minimum quantity lubrication with natural material derivatives in turning SS304 DOI

Javvadi Eswara Manikanta,

Batta Naga Raju,

Nitin Ambhore

и другие.

International Journal on Interactive Design and Manufacturing (IJIDeM), Год журнала: 2024, Номер 18(2), С. 789 - 800

Опубликована: Янв. 6, 2024

Язык: Английский

Процитировано

Mechanical and erosion performance of sugarcane biochar-reinforced polymer composites DOI

R. Sundarakannan,

V. Arumugaprabu,

T. Sathish

и другие.

Biomass Conversion and Biorefinery, Год журнала: 2022, Номер 14(14), С. 15453 - 15468

Опубликована: Дек. 5, 2022

Язык: Английский

Процитировано

Predicting physico-mechanical and thermal properties of loofa cylindrica fibers and Al2O3/Al-SiC reinforced polymer hybrid composites using artificial neural network techniques DOI

H. Mohit,

Sanjay Mavinkere Rangappa,

Suchart Siengchin

и другие.

Construction and Building Materials, Год журнала: 2023, Номер 409, С. 133901 - 133901

Опубликована: Окт. 27, 2023

Язык: Английский

Процитировано