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.

Comparative Investigation of Mechanical Properties in GMAW/GTAW for Various Shielding Gas Compositions

Abstract: Gas metal arc welding (GMAW) and gas tungsten arc welding (GTAW) are extensively used in the areas requiring high-speed welds, higher weld quality finishes, and superior strength properties. In this experimental study, the influence of shielding gas composition on the mechanical properties of austenitic stainless steel 316L was investigated. The selection of shielding gases and current intensities is the major concern to achieve the intended properties of the weld metal. The influence of four various shielding gas compositions was studied in this work. It was carried out in both GMAW and GTAW for 3 and 6 mm thick plates. Optimization of shielding gas mixtures, current intensities, flow rates, and welding speed is required to automate the process and to improve the overall efficiency of the weld process. Mechanical properties are analyzed for GTAW and GMAW specimens. The obtained results prove that the tensile strength, hardness, and impact strength are higher for GTAW weld specimens compared to GMAW weld ...

Adaptive cluster formation in MANET using particle swarm optimization

Abstract: “Mobile Ad Hoc Network” (MANET) is a form of ad hoc network in which the devices are dynamic and configure themselves during the fly. Hence, for an energy efficient and stable communication to be established among the nodes, the nodes must be organized into groups called clusters. Associativity is one among the good metrics that can configure the speed of a node with respect to its neighbour. In the proposed work, along with associativity, residual energy and nodes degree are the multiple metrics that are been considered to elect the clusterheads. Optimal clustering of nodes in dynamic MANET is an NP-Hard problem that can be solved by tools that include evolutionary computation, swarm optimization, etc. Among the swarm optimization methods, genetic algorithm (GA) and particle swarm optimization (PSO) techniques were been proved to be the efficient and promising methodologies for solving the optimization problems. These techniques are finding their popularity in wide range due to their adaptable nature and ability to optimize even large complex search spaces applied to non-differentiable cost functions. Hence, the main objective of the proposed work is to select stable clusterhead by considering multiple metrics that are used to frame the fitness function for GA and PSO. Since the proposed work includes energy as one of the metrics, it can be used in distributed MANET environment with nodes having different energy levels. Since, the associativity of nodes reflects the stability with their neighbours, the proposed system can also be used in dynamic and static environment. The proposed work has been simulated with both GA and PSO, and the results have shown that PSO resulted with better performance compared to GA, in terms of clusterhead count under various simulation environments.

Modeling and optimization of glutamic acid production using mixed culture of Corynebacterium glutamicum NCIM2168 and Pseudomonas reptilivora NCIM2598.

Abstract: In this study, a hybrid system of response surface methodology followed by genetic algorithm has been adopted to optimize the production medium for L-glutamic acid fermentation with mixed cultures of Corynebacterium glutamicum and Pseudomonas reptilovora. The optimal combination of media components for maximal production of L-glutamic acid was found to be 49.99 g L−1 of glucose, 10 g L−1 of urea, 18.06% (v/v) of salt solution, and 4.99% (v/v) of inoculum size. The experimental glutamic acid yield at optimum condition was 19.69 g L−1, which coincided well to the value predicted by the model (19.61 g L−1). Using this methodology, a nonlinear regression model was developed for the glutamic acid production. The model was validated statistically and the determination coefficient (R 2) was found to be 0.99.