A multi-objective algorithm for optimization of modern machining processes

Multi-objective optimization aspects of four modern machining processes namely wire-electro discharge machining process, laser cutting process, electrochemical machining process and focused ion beam micro-milling process are considered in this work. In WEDM process cutting velocity and surface quality are important objectives which are mutually conflicting in nature. Minimization of kerf taper is vital in the laser cutting process which increases with the increase in material removal rate. The ECM process is characterized by high material removal rate, but poor dimensional accuracy, high tool wear rate and high over cut. FIB micro-milling process is useful in applications where a nano-level surface finish is desired but this process is characterized by a very low material removal rate. All the above mentioned objectives are vital as they closely govern the performance of the machining processes considered in this work. Therefore, the aim of this work is to achieve these objectives through process parameter optimization. In order to handle multiple objectives simultaneously a new posteriori multi-objective optimization algorithm named as multi-objective Jaya (MO-Jaya) algorithm is proposed which can provide multiple optimal solutions in a single simulation run. The regression models for the above mentioned machining processes which were developed by previous researchers are used as fitness function for MO-Jaya algorithm.In the case of WEDM process the optimization problem is an unconstrained, linear and parameter bounded. In the case of laser cutting process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In the ECM process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. The second case study of ECM process the optimization problem is a non-linear, constrained, non-quadratic and parameter bounded. In the case of FIB micro-milling process, the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In addition, the performance of MO-Jaya algorithm is also tested on a non-linear, non-quadratic unconstrained multi-objective benchmark function of CEC2009. In order to handle the constraints effectively a heuristic approach for handling constraints known as the constrained-dominance concept is used in MO-Jaya algorithm. In order to ensure that the newly generated solutions are within the parameter bounds a parameter-bounding strategy is used in MO-Jaya algorithm. The results of MO-Jaya algorithm are compared with the results of GA, NSGA, NSGA-II, BBO, NSTLBO, PSO, SQP and Monte Carlo simulations. The results have shown the better performance of the proposed algorithm. Flowchart for the MO-Jaya algorithm.Display Omitted A new multi-objective optimization algorithm named as MO-Jaya algorithm is proposed.Multi-objective optimization aspects of modern machining processes are considered.A Pareto optimal set of solutions along with a Pareto front is obtained for each of the considered machining processes.The MO-Jaya algorithm may also be applied to multi-objective optimization problems of other manufacturing processes.

[1]  M. S. Shunmugam,et al.  Multi-objective optimization of wire-electro discharge machining process by Non-Dominated Sorting Genetic Algorithm , 2005 .

[2]  R. Venkata Rao,et al.  Teaching–Learning-based Optimization Algorithm , 2016 .

[3]  R. Rao.  Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems , 2016 .

[4]  R. Venkata Rao,et al.  Optimization of modern machining processes using advanced optimization techniques: a review , 2014 .

[5]  M. El-Hawary,et al.  Multi-objective Optimization Approach for Optimal Distributed Generation Sizing and Placement , 2015 .

[6]  A. Pandey,et al.  Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet , 2012 .

[7]  Mahmoud Reza Delavar,et al.  Wireless sensors deployment optimization using a constrained Pareto-based multi-objective evolutionary approach , 2016, Eng. Appl. Artif. Intell..

[8]  Neelesh Kumar Jain,et al.  OPTIMIZATION OF ELECTRO-CHEMICAL MACHINING PROCESS PARAMETERS USING GENETIC ALGORITHMS , 2007 .

[9]  P. Siarry,et al.  Multiobjective Optimization: Principles and Case Studies , 2004 .

[10]  R. Venkata Rao,et al.  Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm , 2016, Journal of Intelligent Manufacturing.

[11]  Ali Ghaffari,et al.  Sequential quadratic programming and analytic hierarchy process for nonlinear multiobjective optimization of a hydropower network , 2010 .

[12]  Vijay K. Jain,et al.  Selection of ECM parameters: a fuzzy set approach , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[13]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[14]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[15]  Bin Wang,et al.  Multi-objective optimization using teaching-learning-based optimization algorithm , 2013, Eng. Appl. Artif. Intell..

[16]  Indrajit Mukherjee,et al.  A review of optimization techniques in metal cutting processes , 2006, Comput. Ind. Eng..

[17]  Guoqiang You,et al.  Multiobjective Shape Optimization for Deployment and Adjustment Properties of Cable-Net of Deployable Antenna , 2015 .

[18]  Sanket N. Bhavsar,et al.  Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide , 2015 .

[19]  Miloš Madić,et al.  Application of multi-stage Monte Carlo method for solving machining optimization problems , 2014 .

[20]  V. K. Jain,et al.  Multi-objective optimization of the ecm process , 1986 .

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

[22]  Dan Simon,et al.  Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling , 2015, Eng. Appl. Artif. Intell..

[23]  Uday S. Dixit,et al.  Application of soft computing techniques in machining performance prediction and optimization: a literature review , 2010 .

[24]  José M. Chaves-González,et al.  Teaching learning based optimization with Pareto tournament for the multiobjective software requirements selection , 2015, Eng. Appl. Artif. Intell..

[25]  Marko Kovacevic,et al.  Software prototype for solving multi-objective machining optimization problems: Application in non-conventional machining processes , 2014, Expert Syst. Appl..

[26]  Yangyang Li,et al.  Change detection in SAR images by artificial immune multi-objective clustering , 2014, Eng. Appl. Artif. Intell..

[27]  Miguel A. Vega-Rodríguez,et al.  MOSFLA-MRPP: Multi-Objective Shuffled Frog-Leaping Algorithm applied to Mobile Robot Path Planning , 2015, Eng. Appl. Artif. Intell..

[28]  Swagatam Das,et al.  Decomposition-based modern metaheuristic algorithms for multi-objective optimal power flow - A comparative study , 2014, Eng. Appl. Artif. Intell..

[29]  Jun Chen,et al.  A new holistic systems approach to the design of heat treated alloy steels using a biologically inspired multi-objective optimisation algorithm , 2015, Eng. Appl. Artif. Intell..

[30]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[31]  Shankar Chakraborty,et al.  Selection of the optimal electrochemical machining process parameters using biogeography-based optimization algorithm , 2013 .

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

[33]  Quan-Ke Pan,et al.  A discrete teaching-learning-based optimisation algorithm for realistic flowshop rescheduling problems , 2015, Eng. Appl. Artif. Intell..

[34]  Naruemon Wattanapongsakorn,et al.  Post Pareto-optimal pruning algorithm for multiple objective optimization using specific extended angle dominance , 2015, Eng. Appl. Artif. Intell..

[35]  B. Bhattacharyya,et al.  Investigation for controlled ellectrochemical machining through response surface methodology-based approach , 1999 .

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

[37]  Siba Sankar Mahapatra,et al.  A particle swarm approach for multi-objective optimization of electrical discharge machining process , 2016, J. Intell. Manuf..

[38]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[39]  R. Venkata Rao,et al.  Teaching Learning Based Optimization Algorithm: And Its Engineering Applications , 2015 .

[40]  Karim Hamza,et al.  Multiobjective Optimization Under Uncertainty in Advanced Abrasive Machining Processes Via a Fuzzy-Evolutionary Approach , 2016 .

[41]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[42]  Sanjib Ganguly,et al.  Simple heuristics-based selection of guides for multi-objective PSO with an application to electrical distribution system planning , 2011, Eng. Appl. Artif. Intell..