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Author

I. Rajendran

Bio: I. Rajendran is an academic researcher from PSG College of Technology. The author has contributed to research in topics: Unsprung mass & Spring (device). The author has an hindex of 1, co-authored 1 publications receiving 127 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, a formulation and solution technique using genetic algorithms (GA) for design optimization of composite leaf springs is presented, where the optimum dimensions of a composite leaf spring have been obtained, which contributes towards achieving the minimum weight with adequate strength and stiffness.

138 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the main optimization methods for composite laminate with uniform stacking sequence through their entire structure are described, their characteristic features are contrasted, and the potential areas requiring more investigation are highlighted.

291 citations

Journal ArticleDOI
01 Jan 2011
TL;DR: The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations and is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA).
Abstract: In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC) In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA) The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations

275 citations

Journal ArticleDOI
TL;DR: Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection, and are highly robust and efficient for most engineering optimising studies as mentioned in this paper.
Abstract: Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection. They are highly robust and efficient for most engineering optimising studies. Although a late entrant in the materials arena, GAs based studies are increasingly making their presence felt in many different aspects of this discipline. In recent times, GAs have been successfully used in numerous problems in the areas of atomistic material design, alloy design, polymer processing, powder compaction and sintering, ferrous production metallurgy, continuous casting, metal rolling, metal cutting, welding, and so on. The present review attempts to present the state of the art in this area. It includes three broad sections given as: fundamentals of genetic algorithms, genetic algorithms in materials design, and genetic algorithms in materials processing. The first section provides the reader with the basic concepts and the intricacies associated with this novel tec...

182 citations

Journal ArticleDOI
TL;DR: A genetic algorithm based approach is developed to optimise fixture layout through integrating a finite element code running in batch mode to compute the objective function values for each generation.

173 citations

Journal ArticleDOI
TL;DR: A new, generic method/model for multi-objective design optimization of laminated composite components using a novel multi- objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm is presented.
Abstract: We present a new, generic method/model for multi-objective design optimization of laminated composite components using a novel multi-objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm. QPSO is a co-variant of the popular Particle Swarm Optimization (PSO) and has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; Failure Mechanism based Failure criteria, Maximum stress failure criteria and the Tsai-Wu Failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences as well as fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Also, the performance of QPSO is compared with the conventional PSO.

172 citations