Intelligent Selection of Machining Parameters in Multipass Turnings Using Firefly Algorithm

Belloufi, Abderrahim; Assas, Mekki; Rezgui, Imane

doi:10.1155/2014/592627

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…Basically, the idea was to switch fireflies with best values of objective function; actually, firefly's crossover is implemented. Firefly at position l, from population 1, replaces firefly at position k in population 2 and vice versa, firefly at position k from population 2 comes at position l in population 1 (Algorithm 2lines 36,37,38). This crossover of fireflies allows population with worse solutions to get into space of better solutions, while the population with better solutions checks if the global minimum is found, or there is even better solution to search for.…”

Section: Post Processing and Results Presentationmentioning

confidence: 99%

“…Venkata Rao and Kalyankar 4 used teaching-learning-based optimization algorithm (TLBO) for operation of multi-pass turning and proved that a lower number of iterations is required for convergence to the optimal solution. Belloufi et al 37 used Firefly algorithm (FA) and hybrid genetic algorithm-sequential quadratic programming (GA-SQP) 38 and obtained lower numerical values of production cost compared with other techniques, but without satisfying all constraints. Srinivas et al 8 applied particle swarm optimization (PSO) and provided a detailed comparison of the exceeded constraints found in ACO, 12 GA 7 and SA-PS.…”

Section: Literature Overviewmentioning

confidence: 99%

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Optimization of multi-pass turning and multi-pass face milling using subpopulation firefly algorithm

Miodragović¹,

Đorđević

Bulatović

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, Subpopulation Firefly Algorithm is proposed for optimization of machining parameters in multi-pass turning and multi-pass face milling operations. Basic Firefly Algorithm is modified with the aim to avoid space of local minimum and to meet the operation constraints in each iteration step. For that purpose, the following modifications are made: one firefly population is divided into two, a crossover operator is introduced and the searching for new design variables is continued until constraint functions are fulfilled. For turning operation, optimization is carried out for one objective: minimization of production cost. For face milling operation, multi-objective optimization is used for minimizing production cost and machining time, and maximizing profit rate at the same time. In both cases of multi-pass machining operations, optimization process implies meeting all operation constraints. For multi-pass turning operation, the best results from literature are confirmed with good convergence and low value of standard deviation. For multi-pass milling operation, better results are achieved compared with existing results from literature. The proposed algorithm showed capability of achieving global optimum for complex optimization problems.

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Section: Post Processing and Results Presentationmentioning

confidence: 99%

Section: Literature Overviewmentioning

confidence: 99%

Optimization of multi-pass turning and multi-pass face milling using subpopulation firefly algorithm

Miodragović¹,

Đorđević

Bulatović

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…They concluded that the TLBO can converge to the optimal solution within a lower number of iterations. Belloufi et al [21] proposed a Firefly Algorithm (FA), but constraint limitation such as cutting force was incorrectly handled.…”

Section: Introductionmentioning

confidence: 99%

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

Jabri

Barkany

Khalfi

2017

Modelling and Simulation in Engineering

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For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.

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“…The optimization framework was based on the optimization model proposed by Agapiuou [3]. The same algorithm was later applied by Belloufi et al [10] for determination of optimized conditions in a multipass turning process. The optimization problem was formulated so as to achieve minimization of production cost under a set of machining constraints.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Approach for Production Time Minimization in Longitudinal Turning

Madić¹,

Radovanović²,

Kovačević³

2017

J. Prod. Eng.

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Intelligent Selection of Machining Parameters in Multipass Turnings Using Firefly Algorithm

Cited by 16 publications

References 19 publications

Optimization of multi-pass turning and multi-pass face milling using subpopulation firefly algorithm

Optimization of multi-pass turning and multi-pass face milling using subpopulation firefly algorithm

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

An Optimization Approach for Production Time Minimization in Longitudinal Turning

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