How to cite this paper
Singh, D & Shukla, R. (2020). Multi-objective optimization of selected non-traditional machining processes using NSGA-II.Decision Science Letters , 9(3), 421-438.
Refrences
Acharya, B.R., Mohanty, C.P. & Mahapatra, S.S. (2013). Multi-objective optimization of electrochemical machining of hardened steel using NSGA-II. Procedia Engineering, 51, 554–560.
Asokan, P., Ravi Kumar, R., Jeyapaul, R. & Santhi, M. (2008). Development of multi-objective optimization models for electrochemical machining process. International Journal of Advance Manufacturing Technology, 39 (1-2), 55–63.
Bansal, K. & Goyal, K. (2013). Investigation into the machining characteristics of composites using chemical assisted ultrasonic machining process. International Journal of Research in Mechanical Engineering Technology, 3(2), 78–84.
Bharti, P.S., Maheswari, S. & Sharma, C. (2012). Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II. Journal of Mechanical Science and Technology, 26, 1875-1883.
Chakravorty, R., Gauri, S. K. & Chakraborty, S. (2013). Optimization of multiple responses of ultrasonic machining (USM) Process : A Comparative. International Journal of Industrial Engineering Computational, 4, 285–296.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast elist multi-objective genetic algorithm: NSGA-II. IEEE Transaction of Evolutionary Computational, 6,182-197.
Dewangan, S., Gangopadhyay, S. & Biswas, C.K. (2015). Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach. Engineering Science and Technolology Internatinal Journal, 1, 1–8.
Gao, L., Huang, J. & Li, X. (2013). An effective cellular particle swarm optimization for parameter optimization of a multi-pass milling process. Applied Soft Computing, 12, 3490–3499.
Goswami, D. & Chakraborty, S. (2015). Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms. Ain Shams Engineering Journal, 6 (1), 315–331.
Inamdar, S. V., Gupta, K. S. & Saraf, D. N. (2004). Multi objective optimization of an industrial crude distillation unit using the elitist non-dominated sorting genetic algorithm. Chemical Engineering Research and Design, 82 (5), 611–623.
Jensen, M.T. (2003). Reducing the run-time complexity of multiobjective EAs: The NSGA-II and Other Algorithms. IEEE Transacactions of Evolutionary Computation, 7(5), 503–515.
Keskin, Y., Halkacı, H.S. & Kizil, M. (2005). An experimental study for determination of the effects of machining parameters on surface roughness in electrical discharge machining (EDM). International Journal of Advance Manufacturing Technology, 28, 1118–1121.
Konak, A., Coit, D.W. & Smith, A.E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Releability Engineering System and Safety, 91, 992–1007.
Kumar, J. & Khamba, J. S. (2008). An experimental study on ultrasonic machining of pure titanium using designed experiments. Journal of Brazilian Society of Mechanical Science and Engineering, 3, 231-238.
Kumar, V. (2013). Optimization and modeling of process parameters involved in ultrasonic machining of glass using design of experiments and regression approach. American Journal of Material Engineering Technology, 1(1),13–18.
Kuriakose, S. & Shunmugam, M.S. (2005). Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. Journal of Material Processing Technology, 170, 133–141.
Kuruc, M., Vopat, T. & Peterka, J. (2015). Surface roughness of poly-crystalline cubic boron nitride after rotary ultrasonic machining. Procedia Engineering, 100, 877–884.
Lalchhuanvela, H., Doloi, B. & Bhattacharyya, B. (2012). Enabling and understanding ultrasonic machining of engineering ceramics using parametric analysis. Material and Manufacturing Processes, 27(4), 443–448.
Lozano Torrubia, P., Axinte, D. & Billingham, J. (2015). Stochastic modelling of abrasive waterjet footprints using finite element analysis. International Journal of Machine Tools and Manufacturer, 95, 39–51.
Mandal, D., Pal, S.K. & Saha, P. (2007). Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominated sorting genetic algorithm-II. Journal of Material Processing Technology, 186, 154–162.
Marler, R.T. & Arora, J.S. (2004). Survey of multi-objective optimization methods for engineering. Structure of Multidisciplinary Optimization, 26(6), 369–395.
Munda, J. & Bhattacharyya, B. (2006). Investigation into electrochemical micromachining (EMM) through response surface methodology based approach. International Journal of Advance Manufacturing and Technology, 35, 821-832.
Muthuramalingam, T. & Mohan, B. (2014). A review on influence of electrical process parameters in EDM process. Archives of Civil and Mechanical Engineering, 15(1), 1–8.
Pasupathy, T., Chandrasekharan, R. & Suresh, R.K. (2006). A multi-objective genetic algorithm for scheduling in flow shops to minimize the make span and total flow time of jobs. International Journal of Advance Manufacturing Technology, 27, 804–815.
Popli, D. & Singh, R.P. (2013). Machining process parameters of USM- A Review. International Journal of Emerging Research in Management Technology, 2(10), 46–50.
Rajabi-Bahaabadi, M. Shariat-Mohaymany, A., Babaei, M. & Ahn, C.W. (2015). Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm. Expert Systems with Applications, 42(12), 5056–5064.
Rajurkar, K.P., Sundaram, M.M. & Malshe, A.P. (2013). Review of electrochemical and electrodischarge machining. Procedia CIRP, 6,13–26.
Ramesh, S., Kannan, S. & Baskar, S. (2012). Application of Modified NSGA-II Algorithm to Multi-Objective Reactive Power Planning. Applied Soft Computing Journal, 12 (2), 741–753.
Rout, S. K., Balaji, K., Choudhury, R., Sahoo, K. & Sarangi, S. K. (2014). Multi-objective parametric optimization of inertance type pulse tube refrigerator using response surface methodology and non-dominated sorting genetic algorithm. Cryogenics, 62, 71–83.
Samanta, S. & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24, 946-957.
Schwartzentruber, J. & Papini, M. (2015). Abrasive waterjet micro-piercing of borosilicate glass. Journal of Material Processing Technology, 219, 143–154.
Senthilkumar, C., Ganesan, G. & Karthikeyan, R. (2011). Parametric optimization of electrochemical machining of Al/15% SiCp composites using NSGA-II. Transactions of Nonferrous Metals Society of China, 21, 2294–2300.
Singh, N. & Gianender (2012). USM for hard or brittle material and effect of process parameters on MRR or surface roughness : A Review. International Journal of Applied Engineering Research, 7 (11), 1-6.
Srinivas, N. & Deb, K. (1994). Multi-objective Optimization using non-dominated sorting in genetic algorithm. Evolutionary Computational, 2, 221–248.
Subramanian, G. & Thiagarajan, S. (2014). Optimization of machining parameters for EDM operations based on central composite design and desirability approach. Journal of Mechanical Science and Technology, 28(3), 1045–1053.
Taboada, H. A., Fatema, B., David, W. C. & Naruemon, W. (2007). Practical Solutions for Multi-Objective Optimization: An Application to System Reliability Design Problems. Reliability Engineering and System Safety, 92(3), 314–322.
Talla, G., Sahoo, D.K., Gangopadhyay, S. & Biswas, C.K. (2015). Modeling and multi-Objective optimization of powder mixed electric discharge machining process of aluminum/alumina metal matrix composite, Engineering Science and Technolology International Journal, 1, 1–5.
Teimouri, R. & Baseri, H. (2014). Optimization of magnetic field assisted EDM using the continuous ACO algorithm, Applied Soft Computing, 14, 381–389.
Tzeng, C.J. & Chen, Y.R. (2013). Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach. International Journal of Precesion Engineering and Manufacturer, 14, 709–717.
Wang, W., Radu, Z. & Hugues, R. (2005). Applying Multi-Objective Genetic Algorithms in Green Building Design Optimization, Building and Environment, 40 (11), 1512–1525.
Wenjun, G., Jianming, W. & Na, G. (2011). Numerical simulation for abrasive water jet machining based on ALE algorithm. International Journal of Advance Manufacturing Technology, 53, 247–253.
Wong, J. Y., Q. Sharma, S. & Rangaiah, G.P. (2016). Design of Shell-and-Tube Heat Exchangers for Multiple Objectives Using Elitist Non-Dominated Sorting Genetic Algorithm with Termination Criteria. Applied Thermal Engineering, 93, 888–899.
Yang, M.D., Chen, Y.P., Lin, Y.H., Ho, Y.F. & Lin, J.Y. (2016). Multiobjective Optimization Using Nondominated Sorting Genetic Algorithm-II for Allocation of Energy Conservation and Renewable Energy Facilities in a Campus. Energy and Buildings, 122, 120–130.
Yue, Z., Huang, C., Zhu, H., Wang. J., Yao, P. & Liu, Z.W. (2014). Optimization of machining parameters in the abrasive water-jet turning of alumina ceramic based on the response surface methodology, International Journal of Advance Manufacturing Technology, 71, 107–114.
Yusoff, Y., Ngadiman, M. S. & Zain, A.M. (2011). Overview of NSGA-II for optimizing machining process parameters, Procedia Engineering, 15, 3978–3983.
Yuvaraj, N. & Kumar, M.P. (2014). Multi response optimization of abrasive water jet cutting process parameters using TOPSIS approach. Material and Manufacturing Processes, 30(7), 37–41.
Asokan, P., Ravi Kumar, R., Jeyapaul, R. & Santhi, M. (2008). Development of multi-objective optimization models for electrochemical machining process. International Journal of Advance Manufacturing Technology, 39 (1-2), 55–63.
Bansal, K. & Goyal, K. (2013). Investigation into the machining characteristics of composites using chemical assisted ultrasonic machining process. International Journal of Research in Mechanical Engineering Technology, 3(2), 78–84.
Bharti, P.S., Maheswari, S. & Sharma, C. (2012). Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II. Journal of Mechanical Science and Technology, 26, 1875-1883.
Chakravorty, R., Gauri, S. K. & Chakraborty, S. (2013). Optimization of multiple responses of ultrasonic machining (USM) Process : A Comparative. International Journal of Industrial Engineering Computational, 4, 285–296.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast elist multi-objective genetic algorithm: NSGA-II. IEEE Transaction of Evolutionary Computational, 6,182-197.
Dewangan, S., Gangopadhyay, S. & Biswas, C.K. (2015). Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach. Engineering Science and Technolology Internatinal Journal, 1, 1–8.
Gao, L., Huang, J. & Li, X. (2013). An effective cellular particle swarm optimization for parameter optimization of a multi-pass milling process. Applied Soft Computing, 12, 3490–3499.
Goswami, D. & Chakraborty, S. (2015). Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms. Ain Shams Engineering Journal, 6 (1), 315–331.
Inamdar, S. V., Gupta, K. S. & Saraf, D. N. (2004). Multi objective optimization of an industrial crude distillation unit using the elitist non-dominated sorting genetic algorithm. Chemical Engineering Research and Design, 82 (5), 611–623.
Jensen, M.T. (2003). Reducing the run-time complexity of multiobjective EAs: The NSGA-II and Other Algorithms. IEEE Transacactions of Evolutionary Computation, 7(5), 503–515.
Keskin, Y., Halkacı, H.S. & Kizil, M. (2005). An experimental study for determination of the effects of machining parameters on surface roughness in electrical discharge machining (EDM). International Journal of Advance Manufacturing Technology, 28, 1118–1121.
Konak, A., Coit, D.W. & Smith, A.E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Releability Engineering System and Safety, 91, 992–1007.
Kumar, J. & Khamba, J. S. (2008). An experimental study on ultrasonic machining of pure titanium using designed experiments. Journal of Brazilian Society of Mechanical Science and Engineering, 3, 231-238.
Kumar, V. (2013). Optimization and modeling of process parameters involved in ultrasonic machining of glass using design of experiments and regression approach. American Journal of Material Engineering Technology, 1(1),13–18.
Kuriakose, S. & Shunmugam, M.S. (2005). Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. Journal of Material Processing Technology, 170, 133–141.
Kuruc, M., Vopat, T. & Peterka, J. (2015). Surface roughness of poly-crystalline cubic boron nitride after rotary ultrasonic machining. Procedia Engineering, 100, 877–884.
Lalchhuanvela, H., Doloi, B. & Bhattacharyya, B. (2012). Enabling and understanding ultrasonic machining of engineering ceramics using parametric analysis. Material and Manufacturing Processes, 27(4), 443–448.
Lozano Torrubia, P., Axinte, D. & Billingham, J. (2015). Stochastic modelling of abrasive waterjet footprints using finite element analysis. International Journal of Machine Tools and Manufacturer, 95, 39–51.
Mandal, D., Pal, S.K. & Saha, P. (2007). Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominated sorting genetic algorithm-II. Journal of Material Processing Technology, 186, 154–162.
Marler, R.T. & Arora, J.S. (2004). Survey of multi-objective optimization methods for engineering. Structure of Multidisciplinary Optimization, 26(6), 369–395.
Munda, J. & Bhattacharyya, B. (2006). Investigation into electrochemical micromachining (EMM) through response surface methodology based approach. International Journal of Advance Manufacturing and Technology, 35, 821-832.
Muthuramalingam, T. & Mohan, B. (2014). A review on influence of electrical process parameters in EDM process. Archives of Civil and Mechanical Engineering, 15(1), 1–8.
Pasupathy, T., Chandrasekharan, R. & Suresh, R.K. (2006). A multi-objective genetic algorithm for scheduling in flow shops to minimize the make span and total flow time of jobs. International Journal of Advance Manufacturing Technology, 27, 804–815.
Popli, D. & Singh, R.P. (2013). Machining process parameters of USM- A Review. International Journal of Emerging Research in Management Technology, 2(10), 46–50.
Rajabi-Bahaabadi, M. Shariat-Mohaymany, A., Babaei, M. & Ahn, C.W. (2015). Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm. Expert Systems with Applications, 42(12), 5056–5064.
Rajurkar, K.P., Sundaram, M.M. & Malshe, A.P. (2013). Review of electrochemical and electrodischarge machining. Procedia CIRP, 6,13–26.
Ramesh, S., Kannan, S. & Baskar, S. (2012). Application of Modified NSGA-II Algorithm to Multi-Objective Reactive Power Planning. Applied Soft Computing Journal, 12 (2), 741–753.
Rout, S. K., Balaji, K., Choudhury, R., Sahoo, K. & Sarangi, S. K. (2014). Multi-objective parametric optimization of inertance type pulse tube refrigerator using response surface methodology and non-dominated sorting genetic algorithm. Cryogenics, 62, 71–83.
Samanta, S. & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24, 946-957.
Schwartzentruber, J. & Papini, M. (2015). Abrasive waterjet micro-piercing of borosilicate glass. Journal of Material Processing Technology, 219, 143–154.
Senthilkumar, C., Ganesan, G. & Karthikeyan, R. (2011). Parametric optimization of electrochemical machining of Al/15% SiCp composites using NSGA-II. Transactions of Nonferrous Metals Society of China, 21, 2294–2300.
Singh, N. & Gianender (2012). USM for hard or brittle material and effect of process parameters on MRR or surface roughness : A Review. International Journal of Applied Engineering Research, 7 (11), 1-6.
Srinivas, N. & Deb, K. (1994). Multi-objective Optimization using non-dominated sorting in genetic algorithm. Evolutionary Computational, 2, 221–248.
Subramanian, G. & Thiagarajan, S. (2014). Optimization of machining parameters for EDM operations based on central composite design and desirability approach. Journal of Mechanical Science and Technology, 28(3), 1045–1053.
Taboada, H. A., Fatema, B., David, W. C. & Naruemon, W. (2007). Practical Solutions for Multi-Objective Optimization: An Application to System Reliability Design Problems. Reliability Engineering and System Safety, 92(3), 314–322.
Talla, G., Sahoo, D.K., Gangopadhyay, S. & Biswas, C.K. (2015). Modeling and multi-Objective optimization of powder mixed electric discharge machining process of aluminum/alumina metal matrix composite, Engineering Science and Technolology International Journal, 1, 1–5.
Teimouri, R. & Baseri, H. (2014). Optimization of magnetic field assisted EDM using the continuous ACO algorithm, Applied Soft Computing, 14, 381–389.
Tzeng, C.J. & Chen, Y.R. (2013). Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach. International Journal of Precesion Engineering and Manufacturer, 14, 709–717.
Wang, W., Radu, Z. & Hugues, R. (2005). Applying Multi-Objective Genetic Algorithms in Green Building Design Optimization, Building and Environment, 40 (11), 1512–1525.
Wenjun, G., Jianming, W. & Na, G. (2011). Numerical simulation for abrasive water jet machining based on ALE algorithm. International Journal of Advance Manufacturing Technology, 53, 247–253.
Wong, J. Y., Q. Sharma, S. & Rangaiah, G.P. (2016). Design of Shell-and-Tube Heat Exchangers for Multiple Objectives Using Elitist Non-Dominated Sorting Genetic Algorithm with Termination Criteria. Applied Thermal Engineering, 93, 888–899.
Yang, M.D., Chen, Y.P., Lin, Y.H., Ho, Y.F. & Lin, J.Y. (2016). Multiobjective Optimization Using Nondominated Sorting Genetic Algorithm-II for Allocation of Energy Conservation and Renewable Energy Facilities in a Campus. Energy and Buildings, 122, 120–130.
Yue, Z., Huang, C., Zhu, H., Wang. J., Yao, P. & Liu, Z.W. (2014). Optimization of machining parameters in the abrasive water-jet turning of alumina ceramic based on the response surface methodology, International Journal of Advance Manufacturing Technology, 71, 107–114.
Yusoff, Y., Ngadiman, M. S. & Zain, A.M. (2011). Overview of NSGA-II for optimizing machining process parameters, Procedia Engineering, 15, 3978–3983.
Yuvaraj, N. & Kumar, M.P. (2014). Multi response optimization of abrasive water jet cutting process parameters using TOPSIS approach. Material and Manufacturing Processes, 30(7), 37–41.