Parametric optimization of ultrasonic machining process 317 the optimal settings of parameters for an USM process using Taguchi method and developed a micro-model for prediction of MRR in USM process using dimensional analysis

Ultrasonic machining process; Optimization; Gravitational search algorithm; Fireworks algorithm; Response Abstract Ultrasonic machining (USM) is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion and an abrasive slurry. Unlike other non-traditional machining processes, such as laser beam and electrical discharge machining, USM process does not thermally damage the workpiece or introduce significant levels of residual stress, which is important for survival of materials in service. For having enhanced machining performance and better machined job characteristics, it is often required to determine the optimal control parameter settings of an USM process. The earlier mathematical approaches for parametric optimization of USM processes have mostly yielded near optimal or sub-optimal solutions. In this paper, two almost unexplored nonconventional optimization techniques, i.e. gravitational search algorithm (GSA) and fireworks algorithm (FWA) are applied for parametric optimization of USM processes. The optimization performance of these two algorithms is compared with that of other popular population-based algorithms, and the effects of their algorithm parameters on the derived optimal solutions and computational speed are also investigated. It is observed that FWA provides the best optimal results for the considered USM processes. 2014 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

[1]  D. Aspinwall,et al.  Review on ultrasonic machining , 1998 .

[2]  J. S. Khamba,et al.  Ultrasonic machining of titanium and its alloys : A review , 2006 .

[3]  Neelesh Kumar Jain,et al.  Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms , 2007 .

[4]  J. S. Khamba,et al.  Taguchi technique for modeling material removal rate in ultrasonic machining of titanium , 2007 .

[5]  Akshay Dvivedi,et al.  Surface quality evaluation in ultrasonic drilling through the Taguchi technique , 2007 .

[6]  J. S. Khamba,et al.  Investigation for ultrasonic machining of titanium and its alloys , 2007 .

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

[8]  Vinod Kumar,et al.  Parametric optimization of ultrasonic machining of co-based super alloy using the Taguchi multi-objective approach , 2009, Prod. Eng..

[9]  Pradeep Kumar,et al.  Taguchi’s optimization of process parameters for production accuracy in ultrasonic drilling of engineering ceramics , 2009, Prod. Eng..

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

[11]  J. S. Khamba,et al.  Mathematical modelling of surface roughness in ultrasonic machining of titanium using Buckingham-Π approach: a review , 2009 .

[12]  Jatinder Kumar,et al.  Modeling the material removal rate in ultrasonic machining of titanium using dimensional analysis , 2010 .

[13]  J. Paulo Davim,et al.  Optimisation of process parameters of mechanical type advanced machining processes using a simulated annealing algorithm , 2010 .

[14]  G. K. Mahanti,et al.  Comparative Performance of Gravitational Search Algorithm and Modified Particle Swarm Optimization Algorithm for Synthesis of Thinned Scanned Concentric Ring Array Antenna , 2010 .

[15]  P. J. Pawar,et al.  Parameter Optimization of Ultrasonic Machining Process Using Nontraditional Optimization Algorithms , 2010 .

[16]  Ying Tan,et al.  Fireworks Algorithm for Optimization , 2010, ICSI.

[17]  M. Sayadi,et al.  A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems , 2010 .

[18]  Shankar Chakraborty,et al.  Optimization of correlated multiple responses of ultrasonic machining (USM) process , 2011 .

[19]  Biswanath Doloi,et al.  Enabling and Understanding Ultrasonic Machining of Engineering Ceramics Using Parametric Analysis , 2012 .

[20]  Hossein Nezamabadi-pour,et al.  A prototype classifier based on gravitational search algorithm , 2012, Appl. Soft Comput..

[21]  Ying Tan,et al.  An empirical study on influence of approximation approaches on enhancing fireworks algorithm , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[22]  Mahdiyeh Eslami,et al.  Gravitational search algorithm for optimization of retaining structures , 2012 .

[23]  Shankar Chakraborty,et al.  Parametric Optimization of Nd:YAG Laser Beam Machining Process Using Artificial Bee Colony Algorithm , 2013 .

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

[25]  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.

[26]  Biswanath Doloi,et al.  Analysis on profile accuracy for ultrasonic machining of alumina ceramics , 2013 .

[27]  Shankar Chakraborty,et al.  Optimization of Multiple Responses of Ultrasonic Machining (USM) Process: A Comparative Study , 2013 .

[28]  B. Doloi,et al.  Optimisation of ultrasonic machining of zirconia bio-ceramics using genetic algorithm , 2013, Int. J. Manuf. Technol. Manag..

[29]  Ali R. Yildiz,et al.  Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach , 2013, Inf. Sci..

[30]  R. Venkata Rao,et al.  Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm , 2013, Eng. Appl. Artif. Intell..

[31]  Rabindra Kumar Sahu,et al.  Optimal gravitational search algorithm for automatic generation control of interconnected power systems , 2014 .

[32]  Shankar Chakraborty,et al.  Differential search algorithm-based parametric optimization of electrochemical micromachining processes , 2014 .

[33]  Bernd Scholz-Reiter,et al.  Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations , 2015, Evolutionary Computation.