Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique

Costa, Antonio; Celano, Giovanni; Fichera, Sergio

doi:10.1007/s00170-010-2861-6

Cited by 50 publications

(30 citation statements)

References 19 publications

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“…From the given results, the proposed schemes may be a promising tool for the optimization of machining process parameters. Also in Yang et al [24] the researchers proposed fuzzy global and personal best-mechanism-based multi-objective PSO (F-MOPSO) to optimize the machining parameters. The proposed algorithm was used to optimize the machining parameters is developed to solve such a multi-objective optimization problem in optimization of multi-pass face milling operation.…”

Section: Pso Methodologymentioning

confidence: 99%

An Overview of PSO in Optimization of Machining Parameters

Rajesh¹,

Prabuswamy²

2018

IJTSRD

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In the current trends of optimizing machining process parameters, various evolutionary techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances. From the review, the most machining process considered in PSO was multi-pass turning while the most considered machining performance was production costs

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Section: Pso Methodologymentioning

confidence: 99%

An Overview of PSO in Optimization of Machining Parameters

Rajesh¹,

Prabuswamy²

2018

IJTSRD

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“…When the objective function is the manufacturing cost, Costa et al [9] present minimizing the production cost associated with multi-pass turning problems. In [10] were determined optimal parameters of machining (cutting speed and feed) for getting minimal cost if turning process.…”

Section: Research Related To Optimization Of Manufacturing Procesmentioning

confidence: 99%

“…The paper [9] is presents an algorithm hybrid PSO for minimizing the production cost associated with multi-pass turning problems. The proposed optimization technique consists of a PSO-based framework wherein a properly embedded SA, namely an SA-based local search, aims both to enhance the PSO search mechanism and to move the PSO away from being closed within local optima.…”

Section: Research Related To Optimization Of Manufacturing Procesmentioning

confidence: 99%

A Review on Optimization of Manufacturing Process Performance

Afteni¹,

Frumuşanu²

2017

IJMO

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In this study is present a systematic analysis of already published works on formulating and solving optimization problems concerning manufacturing process. Analysis it was performed on two levels, namely: planning and scheduling of manufacturing process. They were considered: type of optimization (mono-criterion or multi-criteria); objective function (the energy consumption, the manufacturing costs, the productivity, the manufactured surface roughness); methods of solve (Genetic Algorithms GA, Particle Swarm Optimization PSO technique, Artificial Neural Networks ANN). The main purpose of this study it is to substantiate a new approach to optimization problems. The proposed approach is of holistic type, based on integrated process planning and scheduling (IPPS) and defines new performance indicators, to be adapted to market current requirements.

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“…Researchers have utilized several different algorithms for hybridization and applied it for solving machining problems. [19][20][21][22][23][24][25][26] …”

Section: Introductionmentioning

confidence: 99%

Evaluating Process Parameters of Multi-Pass Turning Process Using Hybrid Genetic Simulated Swarm Algorithm

Gayatri¹

2015

J. Adv. Manuf. Syst.

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Evolutionary computation is one of the important problems solving method frequently used by the researchers. The choice of an algorithm to optimize the problem is determined by some sort of reliability of the researcher with that technique. To overcome the limitations in individual algorithms and to achieve synergic effects, fusion or hybridization of two or more algorithms is carried out. Hybrid algorithms have gained popularity because there is no evidence that a universal optimal strategy exists for solving optimization problems. In this work, a hybrid algorithm called hybrid genetic simulated swarm (HGSS) algorithm is proposed to optimize the parameters of multi-pass turning operation. The HGSS algorithm is a fusion of genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms. The objectives of this work are (i) to explore and exploit the problem search space through hybridization, (ii) to justify that proficient hybridization of evolutionary algorithms (EAs) will yield an efficient means to solve the optimization problems. In this work, the EAs such as GA, SA and PSO are also applied to optimize parameters and results are compared with HGSS. The results of the proposed work HGSS are very effective than other algorithms.

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Optimization of multi-pass turning economies through a hybrid particle swarm optimization technique

Cited by 50 publications

References 19 publications

An Overview of PSO in Optimization of Machining Parameters

An Overview of PSO in Optimization of Machining Parameters

A Review on Optimization of Manufacturing Process Performance

Evaluating Process Parameters of Multi-Pass Turning Process Using Hybrid Genetic Simulated Swarm Algorithm

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