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Modelling and Characterization of Basalt/Vinyl Ester/SiC Micro-and Nano-hybrid Biocomposites Properties Using Novel ANN–GA Approach

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Journal of Bionic Engineering 2024年第2期21卷 938-952页

作者： yesudhasan Thooyavan Lakshmi Annamali Kumaraswamidhas Robinson Dhas Edwin Raj Joseph Selvi Binoj Bright Brailson Mansingh anto.y sagai francis britto.alamry ali Department of Mechanical Engineering Indian Institute of Technology Dhanbad(ISM)DhanbadJharkhand826004India Gati Shakti Vishwavidyalaya(GSV-A Central University Funded By GoI in the Ministry of Railways) LalbaugVadodaraGujarat390004India Institute of Mechanical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)Saveetha UniversityChennaiTamilnadu602105India Metal Joining Research Centre Department of Mechanical EngineeringSri Ramakrishna Engineering CollegeCoimbatoreTamilnadu641022India Department of Mechanical Engineering Rohini College of Engineering and TechnologyPalkulamTamil Nadu629401India Department of Mechanical Engineering College of Engineering in Al-KharjPrince Sattam Bin Abdulaziz University11942Al-KharjSaudi Arabia

Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic *** fiber strengthened vinyl ester matrix polymeric composite with filler addition of nano-and micro-sized silicon carbide(SiC)element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear *** application of Artificial Neural Network(ANN)to correlate the filler addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of *** stuffing blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm(GA)to maximize the mechanical and wear-resistant *** predicted and tested ANN–GA optimal values obtained for the composite combination had a tensile,flexural,impact resilience,hardness and wear properties of 202.93 MPa,501.67 MPa,3.460 J/s,43 HV and 0.196 g,respectively,for its optimum combination of filler and *** can be noted that the nano-sized SiC filler particle enhances most of the properties of the composite which diversifies its *** predicted mechanical and wear values of the developed ANN–GA model were in closer agreement with the experimental values which validate the model.

关键词： Hybrid polymer composite Prediction Process modelling Artificial neural networks Genetic algorithm

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