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Chakraborty K.,MCKV Institute of Engineering | Chakraborty M.,Bengal Engineering and Science University | Kar T.K.,Bengal Engineering and Science University
Nonlinear Analysis: Hybrid Systems | Year: 2011

In this paper, we analyze the dynamical behaviour of a bioeconomic model system using differential algebraic equations. The system describes a prey-predator fishery with prey dispersal in a two-patch environment, one of which is a free fishing zone and other is a protected zone. It is observed that a singularity-induced bifurcation phenomenon appears when a variation of the economic interest of harvesting is taken into account. We have incorporated a state feedback controller to stabilize the model system in the case of positive economic interest. A discrete-type gestational delay of predators is incorporated, and its effect on the dynamical behaviour of the model is analyzed. The occurrence of Hopf bifurcation of the proposed model with positive economic profit is shown in the neighbourhood of the coexisting equilibrium point through considering the delay as a bifurcation parameter. Finally, some numerical simulations are given to verify the analytical results, and the system is analyzed through graphical illustrations. © 2011 Elsevier Ltd. Source


Ghosal S.,Jadavpur University | Chaki S.,MCKV Institute of Engineering
International Journal of Advanced Manufacturing Technology | Year: 2010

The paper presents an artificial neural networkoptimization hybrid model to predict and optimize penetration depth of CO2 LASER-MIG hybrid welding used for 5005 Al-Mg alloy. The input welding parameters are power, focal distance from the work piece surface, torch angle, and the distance between the laser and the welding torch. The model combines single hidden layer back propagation artificial neural networks (ANN) with Bayesian regularization for prediction and quasi-Newton search algorithm for optimization. In this method, training and prediction performance of different ANN architectures are initially tested, and the architecture with the best performance is further used for optimization. Finally, the best ANN architecture is found to show much better prediction capability compared to a regression model developed from the experimental data. © Springer-Verlag London Limited 2009. Source


Maity S.R.,Haldia Institute of Technology | Chatterjee P.,MCKV Institute of Engineering | Chakraborty S.,Jadavpur University
Materials and Design | Year: 2012

In today's metalworking industry, many types of materials, ranging from high carbon steel to ceramics and diamonds, are used as cutting tools. Because of the wide range of conditions and requirements, no single cutting tool material meets all the needs of machining applications. Each tool material has its own properties and characteristics that make it best for a specific machining application. While evaluating a cutting tool material for a machining operation, the applicability is dependant on having the correct combination of its physical properties. Thus, it is extensively important to select the most appropriate cutting tool material with the desired properties for enhanced machining performance. This paper considers an exhaustive list of 19 cutting tool materials whose performance are evaluated based on ten selection criteria. The grey complex proportional assessment (COPRAS-G) method is then applied to solve this cutting tool material selection problem considering grey data in the decision matrix. Synthetic single crystal and polycrystal diamonds emerge out as the best two choices. Oil quenched tool steel (AISI O2) and powder metal tool steel (AISI A11) may also be used as the suitable cutting tool materials. Sialon and sintered reaction bonded silicon nitride are the worst chosen cutting tool materials. © 2011 Elsevier Ltd. Source


Chatterjee P.,MCKV Institute of Engineering | Chakraborty S.,Jadavpur University
Materials and Design | Year: 2012

The role of materials in the engineering design process has already been well recognized. Choice of an appropriate material for a particular product is one of the critical tasks for the designers. Designers need to identify materials with specific functionalities in order to find feasible design concepts and fulfill the product's end requirements. There is a vast array of materials with diverse properties available to the designers to satisfy different design requirements. The large number of available materials together with the complex relationships between various selection criteria, often make the material selection process a difficult and time consuming task. A systematic and efficient approach towards material selection is necessary in order to select the best alternative for a given engineering application. This paper focuses on the application of four preference ranking-based multi-criteria decision-making (MCDM) methods for solving a gear material selection problem. These are extended PROMETHEE II (EXPROM2), complex proportional assessment of alternatives with gray relations (COPRAS-G), ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) and operational competitiveness rating analysis (OCRA) methods. Using these four methods, a list of all the possible choices from the best to the worst suitable materials is obtained taking into account different material selection criteria. The ranking performance of these methods is also compared with that of the past researchers. © 2011 Elsevier Ltd. Source


Chatterjee P.,MCKV Institute of Engineering | Chakraborty S.,Jadavpur University
International Journal of Advanced Manufacturing Technology | Year: 2013

Traditional edged cutting tool-based machining processes are now being continuously replaced by nontraditional machining (NTM) processes so as to generate complex and intricate shapes on advanced and harder materials, like titanium, stainless steel, high-strength temperature-resistant alloys, fiber-reinforced composites, and engineering ceramics. These NTM processes, while using energy in its direct form for removing materials from the workpiece surfaces, have the capabilities of meeting some higher level requirements, such as low tolerance, high surface finish, higher production rate, automated data transmission, miniaturization, etc., and are also quite suitable in the areas of micro- and nano-machining. Selection of the most appropriate NTM process to generate a desired shape feature on a given work material is often a challenging task as it involves consideration of diverse machining characteristics and performance of the NTM processes. This paper explores in details the applicability, suitability, and potentiality of evaluation of mixed data method for solving the NTM process selection problems. Three illustrative examples are presented, which validate the usefulness of this method. The observed results exactly corroborate with those obtained by the past researchers. © 2013 Springer-Verlag London. Source

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