Resumen

FE-PERDOMO, Iván La; QUIZA, Ramón  y  RIVAS-SANTANA, Marcelino. Multi-passes turning optimization for sustainable productions by using genetic algorithm and particle swarm heuristics. Dyna rev.fac.nac.minas [online]. 2018, vol.85, n.204, pp.59-64. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v85n204.68623.

Selecting optimal cutting parameters is a very important task in any machining process planning, especially when sustainability is in the sight. This paper presents a multi-objective optimization focused on sustainable productions, for selecting optimal cutting parameters (cutting speed, feed, and depth of cut) in multi-pass cylindrical turning operations. Both, the economic and environmental pillars of sustainability are considered as optimization targets. Technical requirements, such as cutting power, forces and surface roughness, are also taken into account as constraints. Optimization was carried out through a posteriori approach, where a set of non-dominated solutions, also known as Pareto front, were obtained and, then, the most suitable combination of targets is selected for the specific workshop conditions. Two gradient-free optimization techniques were used for carrying out the optimization: the non-sorting genetic algorithm II and the multi-objective particle swarm optimization. A study case was carried out not only for evaluating the fitness of the proposed approach but also for comparing the performance of the considered techniques. The outcomes showed a better performance by the genetic algorithms, in both the computational efficiency and the quality of the obtained Pareto front. The proposed approach demonstrated its convenience for sustainability optimization of machining processes, giving a simpler way for analyzing simultaneously the economic and environmental aspects of sustainability.

Palabras clave : multi-pass cylindrical turning; multiobjective optimization; cutting parameters.

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