Bu Institute Of Engineering

Bānkura, India

Bu Institute Of Engineering

Bānkura, India
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Patra A.,Hooghly Engineering and Technology College | Ganguly S.,Indian Institute of Science | Chattopadhyay P.P.,Indian National Institute of Foundry and Forge Technology | Datta S.,Bu Institute Of Engineering
Multidiscipline Modeling in Materials and Structures | Year: 2015

Purpose - The purpose of this paper is to design and develop precipitation hardened Al-Mg alloy imparting enhanced strength with acceptable ductility through minor addition of Sc and Cr by using multi-objective genetic algorithm-based searching. In earlier attempts of strengthening aluminum alloys, owing to the formation of Al3Sc and Al7Cr phase, addition of Sc and Cr have yielded attractive precipitation hardening, respectively. Both the Al-Sc and Al-Cr system are quench sensitive due to presence of a sloping solvus in their phase diagrams. It is also known that both the Al3Sc and Al7Cr phases nucleate directly from the supersaturated solid solution without formation of GP-zones or transient phases prior to the formation of the Al3Sc and Al7Cr. Sc also found to have beneficial effect on the corrosion property of such alloys. In view of the above, it is of interest to explore the possibility of enhancing the age hardening effect in Al-Mg alloy by addition of Sc and Cr. Design/methodology/approach - The paper uses an approach where experimental information of two different alloy systems (namely, Al-Mg-Sc and Al-Cr) has been combined to generate a single database involving the potential features of both the systems with the aim to formulate the suitable artificial neural network (ANN) models for strength and ductility. The models are used as the objective functions for the optimization process. The patterns of the optimized Pareto front are analyzed to recognize the optimal property of the alloy system. The hitherto unexplored Al-Mg-Sc-Cr alloy, designed from the Pareto solutions and suitably modified on the basis of prior knowledge of the system, is then synthesized and characterized. Findings - The paper has demonstrated the ANN- and genetic algorithm (GA)-based design of a hitherto unexplored alloy by utilizing the existing information concerning the component alloy systems. The paper also established that analyses of the Pareto solutions generated through multi-objective optimization using GA provide an insight of the variation of the parameters at different combination of strength and ductility. It also revealed that the Al-Mg-Sc-Cr alloy has exhibited a two-stage age hardening effect. The first and second stages are due to the precipitation of Al3Sc and Al7Cr phases, respectively. Research limitations/implications - In the present study the two alloy systems are used in tandem to develop models to describe the properties involving the distinct mechanistic features of phase evolution inherent in both the systems. Though the ANN models having the capability to capture huge non-linearity of a system have been employed to predict the convoluted effects of those characteristics when an alloy containing Mg, Sc and Cr are added simultaneously, but the ANN models predictions can be checked experimentally by the future researchers. Practical implications - The paper demonstrates the role of scandium and chromium addition on the ageing characteristics of the alloy by analyzing the age hardening behavior of the designed alloy in cast and cold rolled condition clearly. Originality/value - The approach stated in this paper is a novel one, in the sense that experimental data of two different alloy systems have been clubbed to generate a single database with the aim to formulate the suitable ANN models for strength and ductility. © 2015 Emerald Group Publishing Limited.

Kaiser M.S.,Bangladesh University of Engineering and Technology | Datta S.,BU Institute of Engineering | Roychowdhury A.,Bengal Engineering and Science University | Banerjee M.K.,Malaviya National Institute of Technology, Jaipur
Canadian Metallurgical Quarterly | Year: 2014

Effect of aging on the mechanical properties of cold worked Al-6Mg alloy with minor additions of scandium is studied. Cast and mechanically worked samples are isochronally aged for 60 min at different temperatures up to 500°C. Evaluation of mechanical properties of the aged alloys is done at various strain rates of testing. The m-values (strain rate sensitivity) of the experimental alloys are desired from the tensile test results. The influence of scandium is much pronounced on yield strength than on the tensile strength. Alloys with higher scandium content have shown higher yield strength. The 'm' values are found to be comparatively high at peak aged condition of alloy with higher scandium content. The fracture of the experimental alloys occurs through microvoid coalescence. © 2014 Canadian Institute of Mining, Metallurgy and Petroleum.

Sultana N.,Bengal Engineering and Science University | Sikdar S.,Bengal Engineering and Science University | Chattopadhyay P.P.,Bengal Engineering and Science University | Datta S.,Bu Institute Of Engineering
Materials Technology | Year: 2014

Informatics based approaches are employed to find a suitable composition of Ti alloy, with high strength, low elastic modulus, adequate biocompatibility and low cost. Artificial neural network, capable of prediction and diagnosis in non-linear and complex systems, is used to obtain the relationship of composition and processing parameters with elastic modulus and yield strength. As the objectives are conflicting, multiobjective optimisation using genetic algorithm is employed to optimally design titanium alloys suitable for prosthetic applications using the above models as objective functions for the mechanical properties. The Pareto solutions provide the desired alloy compositions where such properties may be achieved. © 2014 W. S. Maney & Son Ltd.

Pattanayak S.,Indian Institute of Science | Dey S.,Indian Institute of Science | Chatterjee S.,Indian Institute of Science | Chowdhury S.G.,Indian National Metallurgical Laboratory | Datta S.,Bu Institute Of Engineering
Computational Materials Science | Year: 2015

Computational intelligence based modeling and optimization techniques are employed primarily to investigate the role of the composition and processing parameters on the mechanical properties of API grade microalloyed pipeline steel and then to design steel having improved performance in respect to its strength, impact toughness and ductility. Artificial Neural Network (ANN) models, capable of prediction and diagnosis in non-linear and complex systems, are used to obtain the relationship of composition and processing parameters with said mechanical properties. Then the models are used as objective functions for the multi-objective genetic algorithms for evolving the tradeoffs between the conflicting objectives of achieving improved strength, ductility and impact toughness. The Pareto optimal solutions are analyzed successfully to study the role of various parameters for designing pipeline steel with such improved performance. © 2015 Elsevier B.V. All rights reserved.

Datta S.,Bu Institute Of Engineering | Mahfouf M.,University of Sheffield | Zhang Q.,University of Kent | Chattopadhyay P.P.,Indian National Institute of Foundry and Forge Technology | Sultana N.,Indian Institute of Science
Journal of the Mechanical Behavior of Biomedical Materials | Year: 2016

Imprecise knowledge on the composition-processing-microstructure-property correlation of titanium alloys combined with experimental data are used for developing rule based models for predicting the strength and elastic modulus of titanium alloys. The developed models are used for designing alloys suitable for orthopedic and dental applications. Reduced Space Searching Algorithm is employed for the multi-objective optimization to find composition, processing and microstructure of titanium alloys suitable for orthopedic applications. The conflicting requirements attributes of the alloys for this particular purpose are high strength with low elastic modulus, along with adequate biocompatibility and low costs. The '. Pareto' solutions developed through multi-objective optimization show that the preferred compositions for the fulfilling the above objectives lead to β or near β-alloys. The concept of decision making employed on the solutions leads to some compositions, which should provide better combination of the required attributes. The experimental development of some of the alloys has been carried out as guided by the model-based design methodology presented in this research. Primary characterizations of the alloys show encouraging results in terms of the mechanical properties. © 2015 Elsevier Ltd.

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