Effectiveness of FPA in Sparse Data Modelling and Optimization

  • R. S. Umamaheswara Raju
  • V. Ramachandra Raju
  • R. Ramesh
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)


The work is to develop an intelligent model to predict the surface roughness and increase the productivity, as surface roughness estimation is a complex task and cannot be easily done for a given cutting parameters due to the complexity of the machining. In order to predict the surface roughness from cutting parameters, a forward mapping predicted model using flower pollination algorithm (FPA) is developed. While predicting surface roughness the FPA model showed a better performance than the existing regression technique, SVM with competitively a minimum percentile of error.


Cutting parameters Surface roughness FPA 


  1. 1.
    R. Ramesh, S. Jyothirmai, K. Lavanya. Intelligent automation of design and manufacturing in machine tools using an open architecture motion controller. Journal of Manufacturing Systems 32 (2013) 248–259.Google Scholar
  2. 2.
    Vikas Upadhyay, P.K. Jain, N.K. Mehta. “In-process prediction of surface roughness in turning of Ti–6Al–4 V alloy using cutting parameters and vibration signals”. Measurement 46 (2013) 154–160.Google Scholar
  3. 3.
    K. Venkata Rao, B.S.N. Murthy, N. Mohan Rao. “Prediction of cutting tool wear, surface roughness and vibration of workpiece in the boring of AISI 316 steel with artificial neural network”. Measurement 51 (2014) 63–70.Google Scholar
  4. 4.
    Farshid Jafarian, Hossein Amirabadi & Mehdi Fattahi. “Improving surface integrity in finish machining of Inconel 718 alloy using intelligent systems”. Int J Adv Manuf Technol (2014) 71:817–827.Google Scholar
  5. 5.
    Azlan Mohd Zain, Habibollah Haron, Safian Sharif. “Prediction of surface roughness, in the end, milling machining using Artificial Neural Network”. Expert Systems with Applications 37 (2010) 1755–1768.Google Scholar
  6. 6.
    Mohsen Marani Barzani, Erfan Zalnezhad, Ahmed A.D. Sarhan, Saeed Farahany, Singh Ramesh. “Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning” Measurement 61 (2015) 150–161.Google Scholar
  7. 7.
    D. Philip Selvaraj, P. Chandramohan, M. Mohanraj. “Optimization of surface roughness, cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method”. Measurement 49 (2014) 205–215.Google Scholar
  8. 8.
    Turgay Kıvak. “Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts”. Measurement 50 (2014) 19–28.Google Scholar
  9. 9.
    Lakhdar Bouzid, Smail Boutabba, Mohamed Athmane Yallese, Salim Belhadi, Francois Girardin. “Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel”. Int J Adv Manuf Technol, 14 June 2014.Google Scholar
  10. 10.
    Guojun Zhang, Jian Li, Yuan Chen, Yu Huang, Xinyu Shao, Mingzhen Li. “Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration”. Int J Adv Manuf Technol, 21 August 2014.Google Scholar
  11. 11.
    Zahia Hessainia, Ahmed Belbah, Mohamed Athmane Yallese, Tarek Mabrouki, Jean-François Rigal. “On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations”. Measurement 46 (2013) 1671–1681.Google Scholar
  12. 12.
    Tao Zhao, Yaoyao Shi, Xiaojun Lin, Jihao Duan, Pengcheng Sun & Jun Zhang. “Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine”. Int J Adv Manuf Technol, 08 June 2014.Google Scholar
  13. 13.
    A. Bougharriou, W. Bouzid, K. Sai. “Analytical modeling of surface profile in turning and burnishing”. Int J Adv Manuf Technol, 26 July 2014.Google Scholar
  14. 14.
    Jianliang Guo. “Surface roughness prediction by combining static and dynamic features in cylindrical traverse grinding”. Int J Adv Manuf Technol, 16 August 2014.Google Scholar
  15. 15.
    Abdel Badie Sharkawy, Mahmoud A. El-Sharief, M-Emad S. Soliman. “Surface roughness prediction in end milling process using intelligent systems”. Int. J. Mach. Learn. & Cyber. (2014) 5:135–150.Google Scholar
  16. 16.
    Dr. Mike S. Lou, Dr. Joseph C. Chen, Dr. Caleb M. Li. “Surface Roughness Prediction Technique For CNC End-Milling”. Journal of Industrial Technology,Volume 15, 1999, Number 1.Google Scholar
  17. 17.
    R. Ramesh, K. S. Ravi Kumar, G. Anil. “Automated intelligent manufacturing system for surface finish control in CNC milling using support vector machines”. Int J Adv Manuf Technol (2009) 42:1103–1117.Google Scholar
  18. 18.
    V.S.S.S. Chakravarthy, S.R. Chowdary Paladuga, M. Rao Prithvi. “Synthesis of circular array antenna for side lobe level and aperture size control using flower pollination algorithm. International journal of antennas and propagation”, volume 2015, article ID 819712, 9 pages.Google Scholar
  19. 19.
    V.S.S.S. Chakravarthy, P. M. Rao. “On the convergence characteristics of flower pollination algorithm for circular array synthesis”, in proceedings of 2nd international conference on electronics and communication systems 9 ICECS’150, PP. 485–489, IEEE, Feb 2015.Google Scholar
  20. 20.
    Yang, X. S. (2012), Flower pollination algorithm for global optimization, in Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, Vol. 7445, pp. 240–249.Google Scholar
  21. 21.
    M. Aaya-Marquez, “Floral constancy in bees: a revision of theories and a comparison with others pollinators”, Revista Colombian de Entomologia, vol 35, no 2, 2009.Google Scholar
  22. 22.
    I. Pavlyukevich, “Levy flights, non-local search and simulated annealing,” journal of computational physics, vol. 226, no. 2, pp. 1830–1844, 2007.Google Scholar
  23. 23.
    S. Lukasik and P.A. Kowalski, “Study of flower pollination algorithm for continuous optimization,” Advances in intelligent systems and computing, vol. 332, pp. 451–459, 2015.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • R. S. Umamaheswara Raju
    • 1
  • V. Ramachandra Raju
    • 2
  • R. Ramesh
    • 1
  1. 1.Department of Mechanical EngineeringMVGRCEVizianagaramIndia
  2. 2.RGUKT (IIIT)APIndia

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