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

Miodragović, Goran; Đorđević, Violeta; Bulatović, Radovan R.; Petrović, Aleksandar

doi:10.1177/0954406218774378

Cited by 10 publications

(12 citation statements)

References 45 publications

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“…Regression analysis was used. As regards solving the mathematical models of machining parameters optimisation, literature supplies a series of models [3][4][5][6][7][8][9][10][14][15][16][17].…”

Section: Discussionmentioning

confidence: 99%

Untitled

2021

RECENT

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As known, all industrial companies from all over the world are aiming at the main objective, as to maximize their profit. In order to reach this objective, companies use certain strategies, principles and techniques. An important strategy regards the optimisation of the available resources, including material, financial, human and informational resources. Companies must also pay an important attention to their technological processes that include certain machining processes for obtaining the products to be delivered to the market place. The present paper is focused on the optimisation of the cutting tool changing regime, which represents an important part of the lead time, in order to make the machining process more efficient. The core of the paper regards the optimisation of multi-tool life and machining parameters so that the time of tool change is diminished. The paper also includes two case studies that refers to drilling process using multi-tool machining.

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Section: Discussionmentioning

confidence: 99%

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2021

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“…The optimization is made for the lowest surface roughness and cutting temperature and there are used two methods: teaching-learning-based optimization and bacterial foraging optimization. Miodragovic et al [33] propose the use of a subpopulation firefly algorithm (SP-FA) for the optimization of multi-pass turning. The objective function refers to minimizing unit production costs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many methods are based on the mathematical models overtaken from Shin and Joo [6], Gupta et al [8], Chen and Tsai [9], Chen [11], Mellal and Williams [26]. There are also presented certain comparative results [14,15,20,25,[27][28][29]33,35]. From the searched literature it results that machining parameters optimization can be done in three modalities: (a) into two separate phases, one for the roughing machining and the second for the finishing machining; (b) global approach, into a single phase; (c) pass optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Machining Parameters Optimization Based on Objective Function Linearization

2022

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Manufacturing process optimization is an ever-actual goal. Within this goal, machining parameters optimization is a very important task. Machining parameters strongly influence the manufacturing costs, process productivity and piece quality. Literature presents a series of optimization methods. The results supplied by these methods are comparable and it is difficult to establish which method is the best. For machining parameters optimization, this paper proposes a novel, simple and efficient method, without additional costs related to new software packages. This approach is based on linear mathematical programming. The optimization mathematical models are, however, nonlinear. Therefore, mathematical model linearization is required. The major and difficult problem is the linearization of the objective function. This represents the key element of the proposed optimization method. In this respect, the paper proposes an original mathematical procedure for calculating the part of the objective function that refers to the analytical integration of the tool life into the model. This calculus procedure was transposed into an original software tool. For demonstrating the validity of the method, a comparison is presented among the results obtained by certain optimization techniques. It results that the proposed method is simple and as good as those presented by the literature.

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“…The optimal values of speed, feed and depth of cut in each pass were determined for minimum total production cost. Miodragović et al [17] endeavored to modify the basic FA technique to optimize different machining parameters in multi-pass turning and multi-pass face milling operations subject to various practical operational constraints. It was concluded that the proposed algorithm had the ability to reach the global optimal solutions for complex optimization problems.…”

Section: Literature Surveymentioning

confidence: 99%

“…multiple roughing passes and a single finishing pass. For roughing operation, length of tool (cutter) path (L r ) can be expressed as [17]:…”

Section: Mathematical Model For Multi-pass Face Milling Operationmentioning

confidence: 99%

Optimization of Multi-Pass Face Milling Parameters Using Metaheuristic Algorithms

Diyaley

Chakraborty

2019

FU Mech Eng

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In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives.

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

Cited by 10 publications

References 45 publications

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Machining Parameters Optimization Based on Objective Function Linearization

Optimization of Multi-Pass Face Milling Parameters Using Metaheuristic Algorithms

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