Bio: Ajitesh Barman is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Einstein tensor & Tensor (intrinsic definition). The author has an hindex of 1, co-authored 1 publications receiving 13 citations.
TL;DR: In this paper, the authors studied the shear deformation analysis and diffusion behavior of copper-Zirconium Bulk Metallic Glasses (BMG) for different combinations of processing parameters.
Abstract: Shear deformation analysis and diffusion behavior of Copper-Zirconium Bulk Metallic Glasses (BMG) is studied for different combinations of processing parameters. Melt holding temperature, melt holding duration, cooling rate of melt and composition of BMG are varied to obtain BMGs of different structures. The as-quenched structures are characterized using Radial Distribution Function (RDF) and Self-Diffusion constant values (of each atom type). The objective of this study is to design a Cu-Zr BMG which can absorb maximum energy and still deform as little as possible while maximizing its diffusivity during shear deformation of the structure. For this, metamodels were constructed by feeding the Molecular Dynamics (MD) results to an Evolutionary Neural Network (EvoNN) so as to generate the desired objective functions which are then optimized through multiobjective genetic algorithm. This led to identification of some hitherto unknown structures, characterized by atomic coordinates, which have good resistance ...
TL;DR: In this paper , a special type of quarter-symmetric non-metric ϕ and η-connection on a Kenmotsu manifold admits Z−tensor, which is a generalization of Einstein tensor that comes from general relativity.
Abstract: The object of this paper is to study Kenmotsu manifolds admitting Z−tensor, which is a generalization of Einstein tensor that comes from general relativity. We define a special type of quarter-symmetric non-metric ϕ and η-connection on a Kenmotsu manifold and we examine some geometric properties of such manifolds with Z−tensor. Some semi-symmetry conditions related to Z−tensor are studied on Kenmotsu manifolds and finally, we observe our results on a 5-dimensional Kenmotsu manifold.
TL;DR: In this article, a genetic algorithm-based multi-objective optimization method was used to optimize two conflicting requirements of tensile stress and time-to-rupture using a number of evolutionary approaches.
Abstract: Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design v...
TL;DR: The current state of the art of materials research using multi-objective genetic and evolutionary algorithms is briefly presented with critical analyses in this paper, focusing on the achievements to date and the specific needs for further improvement.
Abstract: The current state of the art of materials research using multi-objective genetic and evolutionary algorithms is briefly presented with critical analyses. The basic concepts of multi-objective optimisation and Pareto optimality are explained in simple terms and the advantages of an evolutionary approach are emphasised. Current materials related research in this area is summarised, focusing on the achievements to date and the specific needs for further improvement.
TL;DR: In this paper, the authors address the optimization of the curing process for thick composite laminates and propose a methodology aiming at the evaluation of the thermal cycle promoting a desired evolution of the de
Abstract: This article addresses the optimization of curing process for thick composite laminates The proposed methodology aims at the evaluation of the thermal cycle promoting a desired evolution of the de
TL;DR: The mathematical definition of Pareto optimality ensures that here, it is impossible to find another feasible transportation that will take a traveller to the destination at the same time, as the faster modes of transportations tend to become more expensive and vice versa.
Abstract: Genetic and evolutionary algorithms are inspired by natural biological processes. In these nature inspired computing paradigms, even non-biological problems are solved constructing a pseudobiologic...
TL;DR: In this article, a genetic algorithm combined with principal components analysis (PCA) coupled Grey Relational Analysis (GRA) is employed to improve the straight turning process of MMCs.
Abstract: The metal matrix composites (MMCs) have gained acceptance in an extensive range of applications owing to their high strength to mass ratio. Machining of such complex MMCs is often challenging. It is essential to optimize the controllable machining parameters to simultaneously attain manifold objectives. In the current work, response surface design is created for experiments, and Genetic algorithm (GA) combined with Principal Components Analysis (PCA) coupled Grey Relational Analysis (GRA) is employed to improve the straight turning process of MMCs. The procedure is demonstrated by machining aluminum-based MMC with 25% SiC particulates. The procedure aims at identifying optimal combination of machining parameters to obtain high surface quality at lower cutting force without increasing the specific power consumption. PCA is helpful in providing the individual uncorrelated quality characteristics called as quality indices that do not have any influence on other responses. Individual quality indices h...