Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm

Raja, S. Bharathi; Pramod, C. V. Srinivas; Krishna, K. Vamshee; Ragunathan, Arvind; Vinesh, Somalaraju

doi:10.1007/s00366-013-0320-3

Cited by 31 publications

(11 citation statements)

References 20 publications

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“…The Firefly algorithm developed by Xin-She Yang [20] belongs to a meta-heuristic nature inspired class of algorithms, based on swarm intelligence, being typically used to solve a variety of optimization problems in real-world applications, from project scheduling problems to rich vehicle routing, and from image processing to engineering machining parameters optimization [21][22][23][24].…”

Section: The Firefly Algorithmmentioning

confidence: 99%

New Evolutionary-Based Techniques for Image Registration

Cocianu¹,

Stan²

2019

Applied Sciences

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The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.

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Section: The Firefly Algorithmmentioning

confidence: 99%

New Evolutionary-Based Techniques for Image Registration

Cocianu¹,

Stan²

2019

Applied Sciences

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“…The technologies and techniques equipped with AI applications are widely used in machining processes. It has been found that several approaches, such as thermal modelling, artificial neural network (ANN) [10][11][12][13], dimensional analysis [14], firefly algorithm [15], grey relational technique (GRA) [16], response surface methodology [17,18], and the Taguchi [19] and finite element methods (FEM) [20], have been adopted to model and simulate surface roughness (SR) and tool shape.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Analysis of H13 Steel during Electrical Discharge Machining Process Using Cu–TiC Sintered Electrode

et al. 2021

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In electrical discharge machining (EDM), the machined surface quality can be affected by the excessive temperature generation during the machining process. To achieve a longer life of the finished part, the machined surface quality plays a key role in maintaining its overall integrity. Surface roughness is an important quality evaluation of a material’s surface that has considerable influence on mechanical performance of the material. Herein, a sintered cermet tooltip with 75% copper and 25% titanium carbide was used as tool electrode for processing H13 steel. The experiments have been performed to investigate the effects of EDM parameters on the machined surface roughness. The findings show that, as the pulse current, pulse length, and pulse interval are increased, the surface roughness tends to rise. The most significant determinant for surface roughness was found to be pulse current. A semi-empirical surface roughness model was created using the characteristics of the EDM technique. Buckingham’s theorem was used to develop a semi-empirical surface roughness prediction model. The semi-empirical model’s predictions were in good agreement with the experimental studies, and the built empirical model based on physical features of the cermet tooltip was tested using dimensional analysis.

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“…Their objective was to find the optimal set of process parameters, using GA to achieve the forces selected by the user. Raja et al (2015) optimized the process parameters of electric discharge machining (EDM) using the firefly algorithm to obtain the desired surface roughness in the minimum possible machining time. Raja and Baskar (2012) used PSO to optimize the process parameters to achieve the desired surface roughness while minimizing machining time for face milling.…”

Section: Introductionmentioning

confidence: 99%

A split-optimization approach for obtaining multiple solutions in single-objective process parameter optimization

et al. 2016

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It can be observed from the experimental data of different processes that different process parameter combinations can lead to the same performance indicators, but during the optimization of process parameters, using current techniques, only one of these combinations can be found when a given objective function is specified. The combination of process parameters obtained after optimization may not always be applicable in actual production or may lead to undesired experimental conditions. In this paper, a split-optimization approach is proposed for obtaining multiple solutions in a single-objective process parameter optimization problem. This is accomplished by splitting the original search space into smaller sub-search spaces and using GA in each sub-search space to optimize the process parameters. Two different methods, i.e., cluster centers and hill and valley splitting strategy, were used to split the original search space, and their efficiency was measured against a method in which the original search space is split into equal smaller sub-search spaces. The proposed approach was used to obtain multiple optimal process parameter combinations for electrochemical micro-machining. The result obtained from the case study showed that the cluster centers and hill and valley splitting strategies were more efficient in splitting the original search space than the method in which the original search space is divided into smaller equal sub-search spaces.

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Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm

Cited by 31 publications

References 20 publications

New Evolutionary-Based Techniques for Image Registration

New Evolutionary-Based Techniques for Image Registration

Surface Roughness Analysis of H13 Steel during Electrical Discharge Machining Process Using Cu–TiC Sintered Electrode

A split-optimization approach for obtaining multiple solutions in single-objective process parameter optimization

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