130 research outputs found
Dry sliding wear behaviour of Ta/NbC filled glass-epoxy composites at elevated temperatures
No full textIn this work an attempt was made to evaluate wear loss, specific wear rate and coefficient of friction of Glass-Epoxy (G-E) composites with and without Tantalum Niobium Carbide (Ta/NbC) filler. A vacuum assisted resin transfer moulding (VARTM) technique was employed to fabricate the composite specimens. The fabricated wear specimens were tested by using pin-on-disk test rig at various temperatures viz., 30, 60, 90 and 120° C at normal applied loads of 10 N and 20 N. Sliding velocity of the disc of 1.5 m/s was maintained and test was continued for each sample up to a sliding distance of 5000 m. The wear loss in both the composites increases with increase in temperature and applied normal load. However, Ta/NbC particulate filler incorporated G-E composite exhibits lower wear rate and higher coefficient of friction as compared to unfilled G-E composite. The features of worn surfaces of the specimens were examined under scanning electron microscopy (SEM) and findings are analysed
Publisher Correction:Dysregulation of ghrelin in diabetes impairs the vascular reparative response to hindlimb ischemia in a mouse model; clinical relevance to peripheral artery disease (Scientific Reports, (2020), 10, 1, (13651), 10.1038/s41598-020-70391-6)
No full textIn the original version of this Article, Rajesh Katare and Daryl O. Schwenke were omitted as equally contributing authors. In addition, Rajesh Katare was omitted as a corresponding author. Correspondence and requests for materials should also be addressed to [email protected]. These errors have now been corrected in the HTML and PDF versions of the Article.</p
Low Velocity Angular Impact Test on E Glass Epoxy Laminates using Multi-Orientation Fixture
No full text
A Systematic Review on Author Identification Methods
No full textAuthor Identification is a technique for identifying author of anonymous text. It has near about 130 year's long history, started with the work by Mendenhall 1987. Applications of Author identification include plagiarism detection, detecting anonymous author, in forensics and so on. In this paper the authors outline features used for Author identification like vocabulary, syntactic and others. Researchers worked on various methods for Author identification they also outline this paper on types of Author Identification methods that include 1. Profile-based Approaches which includes Probabilistic Models, Compression Models, Common n-Grams (CNG) approach, 2. Instance-based Approaches which includes Vector Space Models, Similarity-based Models, Meta-learning Models and 3. Hybrid Approaches. At the end the authors conclude this paper with observations and future scope.</p
Analysis of factors influencing deflection in sandwich panels subjected to low-velocity impact
No full textEffect of Nanoclay in Epoxy-based Fibre-glass Composite Laminates subjected to Low Velocity Impact
No full textMathematical modelling to predict the tensile strength of additively manufactured AlSi10Mg alloy using artificial neural networks
No full textIntegrating machine learning in additive manufacturing to simulate real manufacturing outcomes can significantly reduce the cost of manufacturing through selective manufacturing. However, limited research exists on developing a prediction model for the mechanical properties of the material. The input variables include key selective laser melting process parameters such as laser power, layer thickness, scan speed, and hatch spacing, with tensile strength as the output. The artificial neural network (ANN) based mathematical model is compared with a second-degree polynomial regression model. The robustness of both models was further assessed with the new data points beyond those used in the development of ANN-based mathematical model and regression model. The results demonstrate that the proposed ANN-based mathematical model offers superior accuracy, with a mean absolute percentage error (MAPE) value of 4.74 % and the R2 (goodness of fit) value of 0.898 in predicting the strength of AlSi10Mg. The ANN-based mathematical method also demonstrates the strong performance on the new data, achieving a regression value of 0.68. This concludes that the model shows sufficient proof to consider a viable option for predicting the tensile strength
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