Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process

Jabri, Abdelouahhab; Barkany, Abdellah El; Khalfi, Ahmed El

doi:10.4236/eng.2013.57072

Cited by 12 publications

(9 citation statements)

References 29 publications

(43 reference statements)

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“…This method is a multi-objective optimization method that has been widely used in solving different types of industrial problems. It has proven its relevance in solving problems of optimization of failure [25,26], problems of optimization of manufacturing costs [30], problems of optimization of safety and maintenance of equipment [31], design optimization issues [31][32][33], and various other types of industrial problems.…”

Section: Multi-objective Optimization Formulationmentioning

confidence: 99%

A Novel Approach for Integrating the Optimization of the Lifetime and Cost of Manufacturing of a New Product during the Design Phase

Guini¹,

Barkany²,

Jabri³

et al. 2021

SAE Int. J. Mater. Manf.

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Maximum lifetime and minimum manufacturing cost for new products are the primary goals of companies for competitiveness. These two objectives are contradictory and the geometric dimensions of the products directly control them. In addition, the earlier design errors of new products are predicted, the easier and more inexpensive their rectification becomes. To achieve these objectives, we propose in this article a novel model that makes it possible to solve the problem of optimizing the lifespan and the manufacturing cost of new products during the phase of their design. The prediction of the life of the products is carried out by an energy damage method implemented on the Finite Element (FE) calculation by using the ABAQUS software. The manufacturing cost prediction is carried out by applying the ABC cost estimation analytical method. In addition, the optimization problem is solved by the method of genetic algorithms. The proposed model can be successfully applied for the optimization of new mechanical products made by subtractive manufacturing. The products which mostly benefits from this model are those used in machines and in the automotive or aeronautic fields. The proposed approach can be directly used by the designer for an optimal preliminary design of new products whose manufacture is done by the same company or subcontracted entirely or partially by other companies.

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Section: Multi-objective Optimization Formulationmentioning

confidence: 99%

A Novel Approach for Integrating the Optimization of the Lifetime and Cost of Manufacturing of a New Product during the Design Phase

Guini¹,

Barkany²,

Jabri³

et al. 2021

SAE Int. J. Mater. Manf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…where Δ represents the difference in the objective value between the generated solution and the current solution (see (19)). " " is the annealing initial temperature…”

Section: Proposed Solution Algorithmmentioning

confidence: 99%

“…Applying the crossover and mutation operations, new different individuals from parent strings are generated. The metropolis acceptance criterion expressed by (19) is then applied to select which of individuals go into the next generation. In fact, the Old_pop and New_pop go to the next generation through competition; throughout this competition, two elements are extracted from the new solution: f_newbest and f_worst (the minimum and the maximum solutions of the offspring individuals, resp.).…”

Section: Genetic Simulated Annealing Operators Perturbationmentioning

confidence: 99%

“…Aryanfar and Solimanpur [18] presented an optimization of multipass turning operation based on GA technique which aims to minimize the unit production cost. Jabri et al [19] proposed GA approach to solve a multiobjective problem where machining cost and tool life cost are simultaneously optimized. Rao and Kalyankar [20] used the Teaching-Learning-Based Optimization algorithm to solve the same problem (multipass turning operation).…”

Section: Introductionmentioning

confidence: 99%

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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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“…41 To optimize cutting speed, depth of cut, number of passes, and feed, in the multi-pass turning process, GA may be employed in two different ways. 19,42 In one way, the mentioned literature presents the use of GA for multi-objective optimization, while in another way it presents the use of GA for optimization with only one objective function and 20 criteria limitations and functions. A great part of the literature 30,42 presents multi-objective optimization in which multi-targets have been combined in two opposite functions, one function that may be presented as costs and another one that may be presented as productivity or quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

Erić

Stefanović

Đorđević

et al. 2016

Advances in Mechanical Engineering

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The difference between the production cost and selling price of the products may be viewed as a criterion that determines an organization's competitiveness and market success. In such circumstances, it is necessary to impact these criteria in order to maximize this difference. The selling products' price, in modern market conditions, is a category which may not be significantly affected. So organizations have one option, which is the production cost reduction. This is the motive for business organizations and the imperative of each organization. The key parameters that influence the costs of production and therefore influence the competitiveness of organizations are the parameters of production machines and processes used to create products. To define optimal parameter values for production machines and processes that will reduce production costs and increase competitiveness of production organizations, the authors have developed a new mathematical model. The model is based on application of the ABC classification method to classify production line processes based on their costs and an application of a genetic algorithm to find the optimal values of production machine parameters used in these processes. It has been applied in three different modern production line processes; the costs obtained by the model application have been compared with the real production costs.

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Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process

Cited by 12 publications

References 29 publications

A Novel Approach for Integrating the Optimization of the Lifetime and Cost of Manufacturing of a New Product during the Design Phase

A Novel Approach for Integrating the Optimization of the Lifetime and Cost of Manufacturing of a New Product during the Design Phase

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

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