Optimization of multi-pass turning operations using hybrid teaching learning-based approach

This paper presents a novel hybrid optimization approach based on teaching–learning based optimization (TLBO) algorithm and Taguchi’s method. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing area. This research is the first application of the TLBO to the optimization of turning operations in the literature The proposed hybrid approach is applied to two case studies for multi-pass turning operations to show its effectiveness in machining operations. The results obtained by the proposed approach for the case studies are compared with those of particle swarm optimization algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing, and Hooke–Jeeves patter search.

[1]  Katsundo Hitomi,et al.  A STUDY OF ECONOMICAL MACHINING: AN ANALYSIS OF THE MAXIMUM-PROFIT CUTTING SPEED , 1964 .

[2]  B. K. Lambert,et al.  Optimization of multi-pass machining operations , 1978 .

[3]  Ali R. Yildiz,et al.  Hybrid immune-simulated annealing algorithm for optimal design and manufacturing , 2009 .

[4]  Singiresu S Rao,et al.  Determination of Optimum Machining Conditions—Deterministic and Probabilistic Approaches , 1976 .

[5]  Yung C. Shin,et al.  Optimization of machining conditions with practical constraints , 1992 .

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Sujin Bureerat,et al.  Multi-objective topology optimization using evolutionary algorithms , 2011 .

[8]  Ali R. Yildiz,et al.  A comparative study of population-based optimization algorithms for turning operations , 2012, Inf. Sci..

[9]  Faiz A. Al-Khayyal,et al.  Machine parameter selection for turning with constraints: an analytical approach based on geometric programming , 1991 .

[10]  Kiran Solanki,et al.  Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach , 2012 .

[11]  P. J. Pawar,et al.  Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm , 2008 .

[12]  Du-Ming Tsai,et al.  A simulated annealing approach for optimization of multi-pass turning operations , 1996 .

[13]  Ali R. Yildiz,et al.  A novel hybrid immune algorithm for global optimization in design and manufacturing , 2009 .

[14]  P. J. Pawar,et al.  Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms , 2010, Appl. Soft Comput..

[15]  Kazuaki Iwata,et al.  Optimization of Cutting Conditions for Multi-Pass Operations Considering Probabilistic Nature in Machining Processes , 1977 .

[16]  S. Bureerat,et al.  Geometrical Design of Plate-Fin Heat Sinks Using Hybridization of MOEA and RSM , 2008, IEEE Transactions on Components and Packaging Technologies.

[17]  G. Boothroyd,et al.  Maximum Rate of Profit Criteria in Machining , 1976 .

[18]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[19]  Ali Riza Yildiz,et al.  A new design optimization framework based on immune algorithm and Taguchi's method , 2009, Comput. Ind..

[20]  Ali R. Yildiz,et al.  Cuckoo search algorithm for the selection of optimal machining parameters in milling operations , 2012, The International Journal of Advanced Manufacturing Technology.

[21]  G. K. Lal,et al.  Determination of optimal subdivision of depth of cut in multipass turning with constraints , 1995 .

[22]  İsmail Durgun,et al.  Structural Design Optimization of Vehicle Components Using Cuckoo Search Algorithm , 2012 .

[23]  R. C. Creese,et al.  A generalized multi-pass machining model for machining parameter selection in turning , 1995 .

[24]  Kazuhiro Saitou,et al.  Topology Synthesis of Multicomponent Structural Assemblies in Continuum Domains , 2011 .

[25]  Ali Rıza Yıldız,et al.  A novel particle swarm optimization approach for product design and manufacturing , 2008 .

[26]  Vedat Toğan,et al.  Design of planar steel frames using Teaching–Learning Based Optimization , 2012 .

[27]  A R Yildiz,et al.  Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation , 2006 .

[28]  Ali R. Yildiz,et al.  A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations , 2013, Appl. Soft Comput..

[29]  R V Rao,et al.  Parameters optimization of advanced machining processes using TLBO algorithm , 2011 .

[30]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[31]  Ali R. Yildiz,et al.  A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing , 2013, Appl. Soft Comput..

[32]  Ali R. Yildiz,et al.  Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations , 2013, Appl. Soft Comput..

[33]  Mu-Chen Chen,et al.  Optimizing machining economics models of turning operations using the scatter search approach , 2004 .

[34]  Ali Rıza Yıldız,et al.  Structural Damage Detection Using Modal Parameters and Particle Swarm Optimization , 2012 .

[35]  D. S. Ermer,et al.  Optimization of the Constrained Machining Economics Problem by Geometric Programming , 1971 .

[36]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[37]  D. S. Ermer,et al.  Optimization of Multipass Turning With Constraints , 1981 .

[38]  Carlos A. Coello Coello,et al.  Hybridizing a genetic algorithm with an artificial immune system for global optimization , 2004 .

[39]  Mu-Chen Chen,et al.  Optimization of multipass turning operations with genetic algorithms: A note , 2003 .

[40]  R. Saravanan,et al.  Optimization of multi-pass turning operations using ant colony system , 2003 .