Artificial Neural Network and Response Surface Methodology Based Analysis on Solid Particle Erosion Behavior of Polymer Matrix Composites

Autor:	Parvesh Antil, Sundeep Kumar Antil, Anil Kumar, Sarbjit Singh, Catalin I. Pruncu
Rok vydání:	2020
Předmět:	Materials science Glass fiber 02 engineering and technology lcsh:Technology Article 09 Engineering response surface methodology Specific strength 0203 mechanical engineering polymer matrix composites General Materials Science Response surface methodology Composite material lcsh:Microscopy lcsh:QC120-168.85 chemistry.chemical_classification lcsh:QH201-278.5 lcsh:T Abrasive Polymer erosion 021001 nanoscience & nanotechnology Durability 020303 mechanical engineering & transports chemistry lcsh:TA1-2040 Erosion Slurry lcsh:Descriptive and experimental mechanics lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:Engineering (General). Civil engineering (General) 03 Chemical Sciences 0210 nano-technology lcsh:TK1-9971 artificial neural network glass fibers
Zdroj:	Materials, Vol 13, Iss 6, p 1381 (2020) Materials Volume 13 Issue 6
ISSN:	1996-1944
DOI:	10.3390/ma13061381
Popis:	Polymer-based fibrous composites are gaining popularity in marine and sports industries because of their prominent features like easy to process, better strength to weight ratio, durability and cost-effectiveness. Still, erosive behavior of composites under cyclic abrasive impact is a significant concern for the research fraternity. In this paper, the S type woven glass fibers reinforced polymer matrix composites (PMCs) are used to analyze the bonding behavior of reinforcement and matrix against the natural abrasive slurry. The response surface methodology is adopted to analyze the effect of various erosion parameters on the erosion resistance. The slurry pressure, impingement angle and nozzle diameter, were used as erosion parameters whereas erosion loss, i.e., weight loss during an erosion phenomenon was considered as a response parameter. The artificial neural network model was used to validate the attained outcomes for an optimum solution. The comparative analysis of response surface methodology (RSM) and artificial neural network (ANN) models shows good agreement with the erosion behavior of glass fiber reinforced polymer matrix composites.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9b4a2019ec25ac000b006160ba3c227 https://doi.org/10.3390/ma13061381 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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