Multi-objective optimization of the turning process using gravitational search algorithm (GSA) and NSGA-II approach

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. © 2016 PEI. University of Maribor. All rights reserved.

[1]  Joaquim Ciurana,et al.  A system for optimising cutting parameters when planning milling operations in high-speed machining , 2005 .

[2]  Habibollah Haron,et al.  Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA , 2011, Appl. Soft Comput..

[3]  Yuan Luo,et al.  Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades , 2011 .

[4]  Miran Brezocnik,et al.  Modeling and Design of Experiments of Laser Cladding Process by Genetic Programming and Nondominated Sorting , 2015 .

[5]  Habibollah Haron,et al.  Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process , 2010, Expert Syst. Appl..

[6]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[7]  N. Baskar,et al.  Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation , 2011 .

[8]  Paul G. Maropoulos,et al.  Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351 , 2010 .

[9]  Dun-Wei Gong,et al.  Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer , 2011, Expert Syst. Appl..

[10]  Siti Zaiton Mohd Hashim,et al.  Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011) , 2012, Expert Syst. Appl..

[11]  G.-C. Vosniakos,et al.  Rough milling optimisation for parts with sculptured surfaces using genetic algorithms in a Stackelberg game , 2009, J. Intell. Manuf..

[12]  Johnson I. Agbinya,et al.  Self-Configuration of Network Services with Biologically Inspired Learning and Adaptation , 2007, Journal of Network and Systems Management.

[13]  N. Suresh Kumar Reddy,et al.  A GENETIC ALGORITHMIC APPROACH FOR OPTIMIZATION OF SURFACE ROUGHNESS PREDICTION MODEL IN DRY MILLING , 2005 .

[14]  Miran Brezocnik,et al.  A comparison of machine learning methods for cutting parameters prediction in high speed turning process , 2016, Journal of Intelligent Manufacturing.

[15]  Naresh Kumar Reddy,et al.  Determination of Optimal Cutting Conditions Using Design of Experiments And Optimization Techniques , 2012 .

[16]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[17]  I. Palcic,et al.  Designing a Layout Using the Modified Triangle Method, and Genetic Algorithms , 2013 .

[18]  Khaider Bouacha,et al.  Hard turning behavior improvement using NSGA-II and PSO-NN hybrid model , 2016 .

[19]  Zoran Jurković,et al.  Optimization of turning using evolutionary algorithms , 2010 .

[20]  Hossam A. Kishawy,et al.  Optimization of CNC ball end milling : a neural network-based model , 2005 .

[21]  Imtiaz Ahmed,et al.  Optimization of Cutting Parameters in Turning Process , 2014 .

[22]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .

[23]  Joze Balic,et al.  Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation , 2014 .

[24]  Yoshio Mizugaki,et al.  Optimal Tool Selection Based on Genetic Algorithm in a Geometric Cutting Simulation. , 1994 .

[25]  Samir B. Billatos,et al.  Knowledge-based optimization for intelligent machining , 1991 .

[26]  Miran Brezocnik,et al.  MODELLING OF A TURNING PROCESS USING THE GRAVITATIONAL SEARCH ALGORITHM , 2014 .

[27]  Roberto Teti,et al.  Genetic algorithm-based optimization of cutting parameters in turning processes , 2013 .