Single and Multi-objective Optimization Methodologies in CNC Machining

  • Nikolaos Fountas
  • Agis Krimpenis
  • Nikolaos M. Vaxevanidis
  • J. Paulo Davim


Aiming to optimize both productivity and quality in modern manufacturing industries, a wide range of optimization techniques and strategies has been presented by numerous researchers. Optimization modules such as Genetic Algorithms, Evolutionary Algorithms and Fuzzy systems are capable of exploiting manufacturing data with high efficiency and reliability, in order to provide optimal sets of solutions for machining processes. The main scope of this chapter is to present the fundamentals in formulating and developing optimization methodologies, which ultimately offer optimal cutting conditions for both prismatic and sculptured surface part machining and actually improve industrial practice.


Genetic Algorithm Particle Swarm Optimization Simulated Annealing Quality Characteristic Candidate Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Vaxevanidis, N.M., Markopoulos, A., Petropoulos, G.: Artificial Intelligence in Manufacturing Research. In: Paulo Davim, J. (ed.) Artificial neural network modelling of surface quality characteristics in abrasive water jet machining of trip steel sheet, ch. 5, pp. 79–99. Nova Publishers (2010)Google Scholar
  2. 2.
    Rao, R.V.: Advanced Modeling and Optimization of Manufacturing Processes. Springer, London (2011)CrossRefGoogle Scholar
  3. 3.
    Petropoulos, P.G.: Optimal selection of machining rate variable by geometric programming. International Journal of Production Research 11, 305–314 (1973)CrossRefGoogle Scholar
  4. 4.
    Sönmez, A.İ., Baykasoğlu, A., Dereli, T., Filiz, İ.H.: Dynamic optimization of multi-pass milling operations via geometric programming. International Journal of Machine Tools and Manufacture 39, 297–320 (1999)CrossRefGoogle Scholar
  5. 5.
    Kiliç, S.E., Cogun, C., Şen, D.T.: Short Note: A computer-aided graphical technique for the optimization of machining conditions. Computers in Industry 22, 319–326 (1993)CrossRefGoogle Scholar
  6. 6.
    Diwekar, U.: Introduction to Applied Optimization, 2nd edn. Springer (2008)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R., Shi, Y.: Swarm Intelligence. Elsevier, Burlington (2001)Google Scholar
  8. 8.
    Zitzler, E., Laumanns, M., Bleuler, S.: A tutorial on evolutionary multi-objective optimization. In: Metaheuristics for Multiobjective Optimisation, pp. 3–37. Springer (2004)Google Scholar
  9. 9.
    Dixit, P.M., Dixit, U.S.: Modeling of Metal Forming and Machining Processes by Finite Element and Soft Computing Methods. Springer, London (2008)Google Scholar
  10. 10.
    Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing. Springer, Berlin (2005)zbMATHGoogle Scholar
  11. 11.
    De Jong, K.A., Spears, W.M.: A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence 5(1), 1–26 (1992)CrossRefzbMATHGoogle Scholar
  12. 12.
    Haykin, S.: Neural networks, a comprehensive foundation. Prentice-Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  13. 13.
    Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic Programming. Athena Scientific, Belmont (1996)zbMATHGoogle Scholar
  14. 14.
    Fletcher, R.: Practical Methods of Optimization. Wiley, NY (1987)zbMATHGoogle Scholar
  15. 15.
    Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, London (1981)zbMATHGoogle Scholar
  16. 16.
    Levenberg, K.: A method for the solution of certain problems in least squares. m Quarterly of Applied Mathematics 2, 164–168 (1944)zbMATHMathSciNetGoogle Scholar
  17. 17.
    Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics 11, 431–441 (1963)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Masters, T.: Advanced Algorithms for Neural Networks: A C++ Sourcebook. John Wiley and Sons, NY (1995)Google Scholar
  19. 19.
    Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Computer 29(3), 31–44 (1996)CrossRefGoogle Scholar
  20. 20.
    Valiant, L.: Functionality in Neural Nets. In: Proceedings of the American Association for Artificial Intelligence, St. Paul, Minnesota, August 21-26, vol. 2, pp. 629–634 (1988)Google Scholar
  21. 21.
    Siegelmann, H.T., Sontag, E.D.: Turing Computability with Neural Networks. Applied Mathematics Letters 4, 77–80 (1999)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Orponen, P.: An overview of the computational power of recurrent neural networks. In: Proceedings of the 9th Finnish AI Conference - STeP 2000 (2000),
  23. 23.
    Sima, J., Orponen, P.: Computing with continuous-time Liapunov systems. In: Proceedings of the 33rd Annual ACM Symposium on Theory of Computing - STOC 2001, Heraklion, Crete, Greece, July 06 - 08, pp. 722–731 (2001)Google Scholar
  24. 24.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  25. 25.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)zbMATHGoogle Scholar
  26. 26.
    Montgomery, D.C.: Design and analysis of experiments, 5th edn. John Wiley and Sons, USA (2001)Google Scholar
  27. 27.
    Chen, S.-L., Chang, C.-C., Chang, C.-H.: Application of a neural network for improving the quality of five-axis machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214(1), 47–59 (2000)CrossRefGoogle Scholar
  28. 28.
    Karpat, Y., Özel, T.: Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization. International Journal of Advanced Manufacturing Technology 35(3-4), 234–247 (2007)CrossRefGoogle Scholar
  29. 29.
    Davim, J.P., Gaitonde, V.N., Karnik, S.R.: Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. Journal of Materials Processing Technology 205, 16–23 (2008)CrossRefGoogle Scholar
  30. 30.
    Pontes, F.J., Ferreira, J.R., Silva, M.B., Paiva, A.P., Balestrassi, P.P.: Artificial neural networks for machining processes surface roughness modeling. International Journal of Advanced Manufacturing Technology 49(9-12), 879–902 (2010)CrossRefGoogle Scholar
  31. 31.
    Goldberg, D.E.: Genetic Algorithms in Search. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  32. 32.
    Holland, J.: Adaptation in Natural and Artificial Systems, 2nd edn. The MIT Press, Massachusetts (1992)Google Scholar
  33. 33.
    Fogel, D.B.: Phenotype, Genotype and Operators in Evolutionary Computation. The, IEEE International Conference on Evolutionary Computation, 193–198 (1995)Google Scholar
  34. 34.
    Hinterding, R.: Mapping, Order-independent Genes and the Knapsack Problem. In: Proceedings of the 1st IEEE Conference on Evolutionary Computing, vol. 1, pp. 13–17 (1994)Google Scholar
  35. 35.
    Tamaki, H., Kita, H., Shimizu, N., Maekawa, K., Nishikawa, Y.: A Comparison Study of Genetic Codings for the Travelling Salesman Problem. In: The 1st IEEE Conference on Evolutionary Computing, Florida, vol. 1, pp. 1–6 (1994)Google Scholar
  36. 36.
    Bui, T.N., Moon, B.: A New Genetic Approach for the Travelling Salesman Problem. In: Proceeding of the 1st IEEE Conference on Evolutionary Computing, vol. 1, pp. 7–12 (1994)Google Scholar
  37. 37.
    Syswerda, G.: A Study Reproduction in Generational and Steady-State Genetic Algorithms. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, pp. 94–101. Morgan Kaufmann Publishers, San Mateo (1991)Google Scholar
  38. 38.
    Manderick, B., Spoessens, P.: Fine-grained parallel Genetic Algorithms. In: The 4th International Conference on Genetic Algorithms, Virginia, pp. 428–433 (1991)Google Scholar
  39. 39.
    Wang, Z.G., Rahman, M., Wong, Y.S., Sun, J.: Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing. International Journal of Machine Tools and Manufacture 45(15), 1726–1734 (2005)CrossRefGoogle Scholar
  40. 40.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, IV, pp. 1942–1948 (1995)Google Scholar
  41. 41.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization - An overview. Swarm Intelligence 1(1), 33–57 (2007)CrossRefGoogle Scholar
  42. 42.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)Google Scholar
  43. 43.
    Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  44. 44.
    Fleischer, M.: Simulated Annealing: Past, Present, and Future. In: Alexopoulos, C., Kang, K., Lilegdon, W., Goldsman, G. (eds.) Proceedings of the, Winter Simulation Conference, pp. 155–161. ACM (1995)Google Scholar
  45. 45.
    Ryan, C.: Evolutionary Algorithms and Metaheuristics. In: Meyers, R.A. (ed.) Encyclopedia of Physical Science and Technology, 3rd edn., pp. 673–685. Elsevier (2001)Google Scholar
  46. 46.
    Dréo, J., Pétrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization: Methods and Case Studies. Springer, Berlin (2006)Google Scholar
  47. 47.
    Davidson, R., Harel, D.: Drawing Graphs Nicely Using Simulated Annealing. ACM Transactions on Graphics 15(4), 301–331 (1996)CrossRefGoogle Scholar
  48. 48.
    Michalewicz, Z., Fogel, D.: How to Solve It: Modern Heuristics, 2nd edn. Springer, Berlin (2004)CrossRefGoogle Scholar
  49. 49.
    Glover, F.: Tabu Search, Part I. ORSA Journal on Computing 1(3), 190–206 (1989)CrossRefzbMATHGoogle Scholar
  50. 50.
    Glover, F.: Tabu Search, Part II. ORSA Journal on Computing 2(1), 4–32 (1990)CrossRefzbMATHGoogle Scholar
  51. 51.
    Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: Algorithms, applications and advances. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, Kluwer Academic Publishers (2002)Google Scholar
  52. 52.
    Dorigo, M., Blum, C.: Ant Colony Optimization Theory: A survey. Journal of Theoretical Computer Science 344, 243–278 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  53. 53.
    Maniezzo, V.: Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal of Computing 11(4), 358–369 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  54. 54.
    Dos Santos Coelho, L., Alotto, P.: Tribes Optimization Algorithm Applied to the Loney’s Solenoid. IEEE Transactions on Magnetics 45(3), 1526–1529 (2009)CrossRefGoogle Scholar
  55. 55.
    Chen, K., Li, T., Cao, T.: Tribe-PSO: A novel global optimization algorithm and its application in molecular docking. Chemometrics and Intelligent Laboratory Systems 82(1-2), 248–259 (2006)CrossRefGoogle Scholar
  56. 56.
    Cooren, Y., Clerc, M., Siarry, P.: Tribes-A parameter-free particle swarm optimization. In: The 7th EU/Meet, Adaptation, Self-Adaptation, Multi-Level Metaheuristics, Paris, France (2006)Google Scholar
  57. 57.
    Krimpenis, A., Vosniakos, G.C.: Optimisation of roughing strategy for sculptured surface machining using genetic algorithms and neural networks. In: CD Proceedings 8th International Conference on Production Engineering, Design and Control, Alexandria, Egypt, December 27-29 (2004), paper ID: MCH-05Google Scholar
  58. 58.
    Mahfoud, S.W., Goldberg, D.E.: Parallel recombinative simulated annealing: A genetic algorithm. Parallel Computing 21(1), 1–28 (1995)CrossRefzbMATHMathSciNetGoogle Scholar
  59. 59.
    Wong, K.P., Wong, Y.W.: Genetic and genetic/simulated-annealing approaches to economic dispatch, Generation, Transmission and Distribution. IEEE Proceedings 141(5), 507–513 (1994)Google Scholar
  60. 60.
    Mantawy, A.H., Abdel-Magid, Y.L., Selim, S.Z.: Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem. IEEE Transactions on Power Systems 14(3), 829–836 (1999)CrossRefGoogle Scholar
  61. 61.
    Esmin, A.A.A., Lambert-Torres, G., Alvarenga, G.B.: Hybrid Evolutionary Algorithm Based on PSO and GA Mutation. In: Proceedings of 6th International Conference on Hybrid Intelligent Systems, Brazil, 57I-62 (2006)Google Scholar
  62. 62.
    Shi, X.H., Wan, L.M., Lee, H.P., Yang, X.W., Wang, L.M., Liang, Y.C.: An improved genetic algorithm with variable population-size and a PSO-GA based hybrid evolutionary algorithm. Machine Learning and Cybernetics 3, 1735–1740 (2003)Google Scholar
  63. 63.
    Grimaccia, F., Mussetta, M., Zich, R.E.: Genetical Swarm Optimization: Self-Adaptive Hybrid Evolutionary Algorithm for Electromagnetics. IEEE Transactions on Antennas and Propagation 55(3), 781–785 (2007)CrossRefGoogle Scholar
  64. 64.
    Peña, J.M., Robles, V., Larrañaga, P., Herves, V., Rosales, F., Pérez, M.S.: GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 361–371. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  65. 65.
    Krimpenis, A., Vosniakos, G.C.: Rough milling optimisation for parts with sculptured surfaces using Genetic Algorithms in a Stackelberg Game. Journal of Intelligent Manufacturing 20(4), 447–461 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikolaos Fountas
    • 1
  • Agis Krimpenis
    • 1
  • Nikolaos M. Vaxevanidis
    • 1
  • J. Paulo Davim
    • 2
  1. 1.Department of Mechanical Engineering Technology EducatorsSchool of Pedagogical and Technological Education (ASPETE)N. Heraklion AttikisGreece
  2. 2.Department of Mechanical EngineeringUniversity of Aveiro, Campus SantiagoAveiroPortugal

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