Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing

Wang, Z.G.; Rahman, Mohammad Azizur; Wong, Yin Kwan; Sun, Junying

doi:10.1016/j.ijmachtools.2005.03.009

Cited by 106 publications

(31 citation statements)

References 28 publications

(39 reference statements)

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“…The combination of these two methods overcomes their weaknesses and unites their strengths. 52,53 Besides the optimizing of production process parameters, it is possible to optimize the installation of the working machines in the production line by combining GA and greedy algorithms. The goal of this optimization may be to minimize the installation cost of the working machines.…”

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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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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“…Wang et al [21] presented the genetic simulated annealing (GSA) and parallel genetic simulated annealing (PGSA) based on the genetic algorithm and simulated annealing to find optimal machining parameters in milling operations. Baskar et al [22] compared the performance of four non-conventional methods: Ant Colony Algorithm, GA, PSO and Tabu Search.…”

Section: Multi Objective Optimizationmentioning

confidence: 99%

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Hassine¹,

Barkallah²,

Bellacicco³

2015

Int. j. simul. model.

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As manufacturing converts raw materials into products, environmental wastes and emissions are simultaneously generated from the consumption of materials and energy during the manufacturing processes. Then, sustainable manufacturing is defined as the creation of manufactured products using processes that minimize negative environmental impacts, conserve energy and natural resources and that are safe on employees, communities and consumers. Such an approach requires a compromise between ecological and economic aspects to meet the pillars of sustainable development.This paper presents the implementation of particle swarm tool in order to solve multi-objective optimization for sustainable manufacturing. Hence, this study might serve as part of a global approach to model sustainable manufacturing. The main objective of this approach is to develop operations that allow production with respect of ecological, economic and technological constraints. We developed a case study on the cutting conditions during turning at the end of our study.

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“…In the present study, simulated annealing (SA) was applied as the global optimization algorithm, because SA is known to be very well suited for solving highly nonlinear problems and, thus, effective in approaching the global optimum [8][9][10]. SA is based on the random evaluation of the objective function, in such a way that escape from a local minimum is possible.…”

Section: Optimization Proceduresmentioning

confidence: 99%

FEM-based optimum design of multi-stage deep drawing process of molybdenum sheet

Kim

Hong

2007

Journal of Materials Processing Technology

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Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing

Cited by 106 publications

References 28 publications

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

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

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

FEM-based optimum design of multi-stage deep drawing process of molybdenum sheet

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