A
A. Elayaperumal
Researcher at Anna University
Publications - 72
Citations - 2777
A. Elayaperumal is an academic researcher from Anna University. The author has contributed to research in topics: Composite number & Coating. The author has an hindex of 20, co-authored 68 publications receiving 2149 citations. Previous affiliations of A. Elayaperumal include College of Engineering, Guindy.
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The effect of stitching on FRP cylindrical shells under axial compression
TL;DR: In this article, the effect of stitching on FRP composites was studied and it was observed that stitching increases delamination strength to a great extent. But stitching can also cause in-plane fiber damage and creation of resin-rich pockets.
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Experimental investigation on the effect of ceramic coating on engine performance and emission characteristics for cleaner production
S. Mohamed Abbas,A. Elayaperumal +1 more
TL;DR: In this paper, the performance and emission characteristics of a three cylinder diesel engine with its piston coated with three different ceramic coating materials namely zirconia, ZrO2+Al2O3 and fused Zirconium (40FZA).
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Wear characteristics of electroless NiP/bio-composite coatings on En8 steel
TL;DR: In this paper, the advantages of using sea shell particles (SSPs) as reinforcement on the tribological performance of the NiP coating on En8 steel substrate were revealed.
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Microstructure and dry sliding wear resistance evaluation of plasma nitrided austenitic stainless steel type AISI 316LN against different sliders
TL;DR: In this paper, a plasminitriitriouide (PN) treated surface has been characterized by Vickers microhardness measurements, Scanning Electron Microscopic (SEM) examination, X-ray Diffraction (XRD) and sliding wear assessment.
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Modeling and evaluation of tensile properties of randomly oriented banana/epoxy composite
TL;DR: In this article, the authors compare the experimentally observed tensile properties of a banana/epoxy composite with those obtained using various theories of reinforcement, such as reinforcement learning and reinforcement learning.