Archives > Volume 11 | Number 4 | December 2016 > pp 366–376
Advances in Production Engineering & Management
Volume 11 | Number 4 | December 2016 | pp 366–376
Multi-objective optimization of the turning process using a Gravitational Search Algorithm (GSA) and NSGA-II approach
Klancnik, S.; Hrelja, M.; Balic, J.; Brezocnik, M.
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A B S T R A C T
In this paper, we proposed a Gravitational Search Algorithm (GSA) and an NSGA-II approach for multi-objective optimization of the CNC turning process. The GSA is a swarm intelligence method exploiting the Newtonian laws on elementary mass objects interaction in the search space. The NSGA-II is an evolutionary algorithm based on non-dominated sorting. On the basis of varying values of the three independent input machining parameters (i.e., cutting speed, depth of cut, and feed rate), the values of the three dependent output variables were measured (i.e., surface roughness, cutting forces, and tool life). The obtained data set was divided further into two subsets, for the training data and the testing data. In the first step of the proposed approach, the GSA and the training data set were applied to modelling a suitable model for each output variable. Then the accuracies of the models were checked by the testing data set. In the second step, the obtained models were used as the objective functions for a multi-objective optimization of the turning process by the NSGA-II. The optimization constraints relating to intervals of dependent and independent variables were set on the theoretical calculations and confirmed with experimental measurements. The goal of the multi-objective optimization was to achieve optimal surface roughness, cutting forces, and increasing of the tool life, which reduces production costs. The research has shown that the proposed integrated GSA and NSGA-II approach can be implemented successfully, not only for modelling and optimization of the CNC turning process, but also for many other manufacturing processes.
A R T I C L E I N F O
Keywords • Turning; Multi-objective optimization; Evolutionary algorithms; Particle swarm; Gravitational search algorithm, NSGA-II algorithm
Corresponding author • Klancnik, S.
Article history • Received 8 July 2016, Revised 19 November 2016, Accepted 21 November 2016
Published on-line • 10 December 2016
E X P O R T C I T A T I O N
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