Genetic algorithms solution to the single-objective machining process optimization time model

Amiolemhen, P.E.; Eseigbe, Joshua Ahurome

doi:10.30464/jmee.2019.3.1.13

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…The total time required to produce a part is the sum of the time required for machining, tool change, tool quick return, and assembly and dismounting time [14,15,[24][25][26]. (2)…”

Section: Production Timementioning

confidence: 99%

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Multi-objective optimization of machining conditions by geometric programming

Djennane,

Benbouta,

Kherraf

2024

SEES

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In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper outlines the development of an optimization strategy to determine the optimum cutting parameters for turning processes. Two objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is production cost and the second one is the production time. The optimal values of the cutting conditions are found based on the objective function developed for the typified criterion by using a non-linear programming technique called “geometric programming”. In the optimization procedure, the objective functions are subject to constraints of maximum and minimum feed rates and speeds available, cutting power, tool life, deflection of work piece, axial pre-load and surface roughness. An example is presented to illustrate the procedure of this technique.

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“…The total time required to produce a part is the sum of the time required for machining, tool change, tool quick return, and assembly and dismounting time [14,15,[24][25][26]. (2)…”

Section: Production Timementioning

confidence: 99%

“…Studies in Engineering and Exact Sciences, Curitiba, v.5, n.1, p. 371-390w2 are the two weighting coefficients of the two terms in equation(25).…”

mentioning

confidence: 99%

Multi-objective optimization of machining conditions by geometric programming

Djennane,

Benbouta,

Kherraf

2024

SEES

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“…The multi-objective Genetic Algorithms methodology was implemented by applying the weighted method developed by[18], given as Figs 1-2. The elements of the proposed models developed using Genetic Algorithm have been implemented in the software developed in Microsoft Visual Basic.Net environment and run on a Pentium 4 PC with 3.0 GHz Intel Processor and 2 GB of RAM.…”

mentioning

confidence: 99%

Multiobjective optimization of multipass turning machining process using the genetic algorithms solution

Amiolemhen

Eseigbe

2019

Journal of Mechanical and Energy Engineering

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The study involves the development of multi-objective optimization model for turning machining process. This model was developed using a GA-based weighted-sum of minimum production cost and time criteria of multipass turning machining process subject to relevant technological/practical constraints. The results of the single-objective machining process optimization models for the multipass turning machining process when compared with those of multi-objective machining process model yielded the minimum production cost and minimum production time as $5.775 and 8.320 min respectively (and the corresponding production time and production cost as 12.996 min and $6.992, respectively), while those of the multi-objective machining process optimization model were $5.841and 9.097 min. Thus, the multi-objective machining process optimization model performed better than each of the single-objective model for the two criteria of minimum production cost and minimum production time respectively. The results also show that minimum production time model performs better than the minimum production cost model. For the example considered, the multi-objective model gave a lower production time of 30.0% than the corresponding production time obtained from the minimum production cost model, while it gave a lower production cost of 16.46% than the corresponding cost obtained by the minimum production time model.

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Genetic algorithms solution to the single-objective machining process optimization time model

Cited by 2 publications

References 28 publications

Multi-objective optimization of machining conditions by geometric programming

Multi-objective optimization of machining conditions by geometric programming

Multiobjective optimization of multipass turning machining process using the genetic algorithms solution

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