Journal of Intelligent Manufacturing

, Volume 29, Issue 8, pp 1715–1737 | Cite as

Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm

  • R. Venkata Rao
  • Dhiraj P. Rai
  • J. Balic


Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting Teaching–Learning-Based Optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.


Sustainable machining processes Micro-machining Parameter optimization Teaching–learning-based optimization algorithm A posteriori approach 



The authors are thankful to the Department of Science and Technology (DST), Ministry of Science and Technology, of the Republic of India and the Slovenian Research Agency (ARRS), Ministry of Education, Science and Sport of the Republic of Slovenia for providing the financial support for the project entitled “Optimization of Sustainable Advanced Manufacturing Processes”.


  1. Abhishek, K., Kumar, R. V., Datta, S., & Mahapatra, S. S. (2015). Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching-learning based optimization algorithm). Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1050-8.CrossRefGoogle Scholar
  2. Bhavsar, S. N., Aravindan, S., & Rao, P. V. (2015). Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide. Precision Engineering, 40, 131–138.CrossRefGoogle Scholar
  3. Chandrasekaran, M., Muralidhar, M., Krishna, M. C., & Dixit, U. S. (2010). Application of soft computing techniques in machining performance prediction and optimization: A literature review. International Journal of Advanced Manufacturing Technology, 46, 445–464.CrossRefGoogle Scholar
  4. Chen, D., Lu, R., Zou, F., & Li, S. (2015). Teaching–learning-based optimization with variable-population scheme and its application for ANN and global optimization. Neurocomputing. doi: 10.1016/j.neucom.2015.08.068.CrossRefGoogle Scholar
  5. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. London: Wiley.Google Scholar
  6. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.CrossRefGoogle Scholar
  7. Garg, M. P., Jain, A., & Bhushan, G. (2012). Modelling and multi-objective optimization of process parameters of wire electrical-discharge machining using non-dominated sorting genetic algorithm-II. Proceedings of Institution of Mechanical Engineers: Part B-Journal of Engineering Manufacture, 226(12), 1986–2001.CrossRefGoogle Scholar
  8. Kovacevic, M., Madic, M., Radovanovic, M., & Rancic, D. (2014). Software prototype for solving multi-objective machining optimization problems: Application in non-conventional machining processes. Expert Systems with Applications, 41, 5657–5668.CrossRefGoogle Scholar
  9. Kuriachen, B., Somashekhar, K. P., & Mathew, J. (2015). Multiresponse optimization of micro-wire electrical discharge machining process. International Journal of Advanced Manufacturing Technology, 76, 91–104.CrossRefGoogle Scholar
  10. Li, D., Zhang, C., Shao, X., & Lin, W. (2014). A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0919-2.CrossRefGoogle Scholar
  11. Medina, M. A., Das, S., Coello, C. A. C., & Ramírez, J. M. (2014). Decomposition-based modern metaheuristic algorithms for multiobjective optimal power flow—A comparative study. Engineering Applications of Artificial Intelligence, 32, 10–20.CrossRefGoogle Scholar
  12. Mellal, M. A., & Williams, E. J. (2014). Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0925-4.CrossRefGoogle Scholar
  13. Mohanty, C. P., Mahapatra, S. S., & Singh, M. R. (2014). A particle swarm approach for multi-objective optimization of electrical discharge machining process. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0942-3.CrossRefGoogle Scholar
  14. Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering, 50, 15–34.CrossRefGoogle Scholar
  15. Palanikumar, K., Latha, B., Senthilkumar, V. S., & Karthikeyan, R. (2009). Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II). Metals and Materials International, 15(2), 249–258.CrossRefGoogle Scholar
  16. Pandey, A. K., & Dubey, A. K. (2012). Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet. Optics and Laser Technology, 44, 1858–1865.CrossRefGoogle Scholar
  17. Patel, V. K., & Savsani, V. J. (2014). A multi-objective improved teaching-learning based. optimization algorithm (MO-ITLBO). Information. doi: 10.1016/j.ins.2014.05.049.CrossRefGoogle Scholar
  18. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303–315.CrossRefGoogle Scholar
  19. Rao, R. V., & Kalyankar, V. D. (2014). Optimization of modern machining processes using advanced optimization techniques: A review. International Journal of Advanced Manufacturing Technology, 73, 1159–1188.CrossRefGoogle Scholar
  20. Rao, R. V., & Patel, V. (2014). A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems. International Journal of Industrial Engineering Computations, 5, 1–22.Google Scholar
  21. Rao, R. V. (2015). Teaching–learning-based optimization (TLBO) algorithm and its engineering applications. London: Springer.Google Scholar
  22. Rao, R. V. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19–34.Google Scholar
  23. Rao, R. V. (2016). Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decision Science Letters, 5, 1–30.Google Scholar
  24. Somashekhar, K. P., Ramachandran, N., & Mathew, J. (2010). Material removal characteristics of microslot (kerf) geometry in \(\mu \)-WEDM on aluminium. International Journal of Advanced Manufacturing Technology, 51, 611–626.CrossRefGoogle Scholar
  25. Sultana, S., & Roy, P. K. (2014). Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Electrical Power and Energy Systems, 63, 534–535.CrossRefGoogle Scholar
  26. Teimouri, R., Baseri, H., & Moharami, R. (2014). Multi-responses optimization of ultrasonic machining process. Journal of Intelligent Manufacturing, 26, 745–753.CrossRefGoogle Scholar
  27. Yu, K., Wang, X., & Wang, Z. (2014). An improved teaching–learning-based optimization algorithm for numerical and engineering optimization problems. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0918-3.CrossRefGoogle Scholar
  28. Yu, K., Wang, X., & Wang, Z. (2015). Self-adaptive multi-objective teaching–learning-based optimization and its application in ethylene cracking furnace operation optimization. Chemometrics and Intelligent Laboratory Systems, 146, 198–210.CrossRefGoogle Scholar
  29. Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Evolutionary techniques in optimizing machining parameters: Review and recent applications. Expert Systems with Applications, 39, 9909–9927.CrossRefGoogle Scholar
  30. Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2014). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing, 25, 1463–1472.CrossRefGoogle Scholar
  31. Zainal, N., Zain, A. M., Radzi, N. H. M., & Othman, M. R. (2014). Glowworm swarm optimization (GSO) for optimization of machining parameters. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0914-7.CrossRefGoogle Scholar
  32. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.CrossRefGoogle Scholar
  33. Zou, F., Wang, L., Hei, X., Chen, D., & Wang, B. (2014). Multi-objective optimization using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26, 1291–1300.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringS. V. National Institute of TechnologySuratIndia
  2. 2.University of MariborMariborSlovenia

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