Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Study on durability of natural fibre concrete composites using mechanical strength and microstructural properties

Abstract: Investigations to overcome the brittle response and limiting post-yield energy absorption of concrete led to the development of fibre reinforced concrete using discrete fibres within the concrete mass. Out of the commonly used fibres, easily available low cost natural fibres are renewable source materials. Though these fibres are ecologically advantageous, they have some limitations such as lower durability and lesser strength. But recent research provides several treatment processes to enhance the durability of natural fibres. In this paper, the durability of natural fibres such as coconut coir and sugarcane bagasse has been reported by conducting an experimental investigation. This investigation includes two parts. The first part focuses on the determination of mechanical strength properties such as compressive, tensile, modulus of rupture and flexural properties of natural fibre reinforced concrete specimens once every 3 months for a period for 2 years under alternate wetting and drying conditions. Gain or loss in strength of composite concrete at 9 intervals were computed and are reported here. The second part covers the microstructural properties of fresh natural fibres in as received condition and natural fibres reacted with concrete under accelerated curing conditions for two years. SEM and EDAC test results are discussed.

Adsorption of basic red 9 onto activated carbon derived from immature cotton seeds: isotherm studies and error analysis

Abstract: The activated carbon produced from immature cotton seeds via sulphuric acid activation was utilized for adsorption of the basic red 9 from the aqueous solution. Adsorbent possessed a larger surface area (495.96 m2/g), methylene blue number (42) and iodine number (510). The process parameters of the sorption system, such as pH of the solution (2–12), temperature (20–40°C) agitation time (1–5 h) and initial BR9 concentration (50–250 mg/l), were studied to understand their effects on BR9 removal. The optimized parameters are found to be pH: 12, temperature: 40°C, agitation time: 3 h and initial concentration: 150 mg/l. The experimental equilibrium data were analysed using a single-parameter model (Henry’s law), six two-parameter models (Langmuir, Freundlich, Temkin, Dubinin–Radushkevich, Smith, and Javanovic), eleven three-parameter models (Redlich–Peterson, Sips, Toth, Hill, Khan, Brunauer–Emmett–Teller (BET), Fritz–Schluender-III, Vieth–Sladek, Radke–Prausnitz, Brouers–Sotolongo, and Unilin), five ...

A study on kerf characteristics of hybrid aluminium 7075 metal matrix composites machined using abrasive water jet machining technology

Abstract: Metal matrix composites are difficult to machine in traditional machining methods. Abrasive water jet machining is a state-of-the art technology which enables machining of practically all engineeri...

Design and optimization of machining fixture layout using ANN and DOE

Abstract: In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks (ANN)-based algorithm with design of experiments (DOE) is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various fixture layouts. ANN is used as an optimization tool to find the optimal location of the locators and clamps. To train the ANN, sufficient sets of input and output are fed to the ANN system. The input includes the position of the locators and clamps. The output includes the maximum deformation of the workpiece for the corresponding fixture layout under the machining condition. In the testing phase, the ANN results are compared with the FEM results. After the testing process, the trained ANN is used to predict the maximum deformation for the possible fixture layouts. DOE is introduced as another optimization tool to find the solution region for all design variables to minimum deformation of the work piece. The maximum deformations of all possible fixture layouts within the solution region are predicted by ANN. Finally, the layout which shows the minimum deformation is selected as optimal fixture layout.