Application of Cuckoo Search Algorithm User Interface for Parameter Optimization of Ultrasonic Machining Process

  • D. SinghEmail author
  • R. S. Shukla
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)


Ultrasonic machining (USM) process is significant as it does not produce residual stress and thermal damage to the machining surface. The process is capable of engraving, cavity sinking, slicing, drilling holes and broaching in non-conductive and brittle materials like ceramic, glass, etc. that are hard and brittle in nature. The optimum parameter is required to obtain the desired profile on the machined surface with less residual damage. To achieve this objective, a graphical user interface (GUI) is developed that mimics metaheuristic technique, Cuckoo search algorithm (CSA). The advantage of CSA interface is that it provides flexibility to the end user to solve continuous domain problems based on other machining process without bother about mathematical computation. The GUI is tested on two cases of USM process and results show the effectiveness of CSA based interface. The effect of USM process parameters are studied and reported for effective study of the considered processes. The results for USM processes obtained using the considered metaheuristics techniques are compared with experimental results of previous researchers and other algorithms, such as particle swarm optimization (PSO) and black hole algorithm (BHA). It is observed that the results obtained using CSA are found effective and better compared to the results given by other algorithms.


Graphical user interface Cuckoo search algorithm Surface roughness Ultrasonic machining process 


  1. 1.
    Sharman AR, Aspinwall DK, Kasuga V (2001) Ultrasonic assisted turning of gamma titanium aluminide. In: Proceedings of 13th international symposium for electromachining, Spain, (Part-I), pp 939–951Google Scholar
  2. 2.
    Singh R, Khamba JS (2006) Ultrasonic machining of titanium and its alloys: a review. J Mat Process Technol 173(2):125–135CrossRefGoogle Scholar
  3. 3.
    Gauri SK, Chakravorty R, Chakraborty S (2010) Optimization of correlated multiple responses of ultrasonic machining (USM) process. Int J Adv Manuf Technol 53(9–12):1115–1127Google Scholar
  4. 4.
    Singh N, Gianender (2012) USM for hard or brittle material and effect of process parameters on MRR or surface roughness : a review. Int J Appl Eng Res 7(11):1 - 6Google Scholar
  5. 5.
    Popli D, Singh RP (2013) Machining process parameters of USM—a review. Int J Emerg Res Manag Technol 9359(10):46–50Google Scholar
  6. 6.
    Goswami D, Chakraborty S (2015) Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms. Ain Shams Engi J 6(1):315–331CrossRefGoogle Scholar
  7. 7.
    Dvivedi A, Kumar P (2007) Surface quality evaluation in ultrasonic drilling through the Taguchi technique. Int J Adv Manuf Technol 34:131–140CrossRefGoogle Scholar
  8. 8.
    Kumar J, Khamba JS (2008) An experimental study on ultrasonic machining of pure titanium using designed experiments. J Brazilian Soc Mech Sci Engi 3:231–238Google Scholar
  9. 9.
    Kumar J, Khamba JS, Mohapatra SK (2009) Investigating and modeling tool-wear rate in the ultrasonic machining of titanium. Int J Adv Manuf Technol 41(11–12):1107–1117CrossRefGoogle Scholar
  10. 10.
    Jadoun RS, Kumar P, Mishra BK (2009) Taguchi’s optimization of process parameters for production accuracy in ultrasonic drilling of engineering ceramics. Prod Eng 3(3):243–253CrossRefGoogle Scholar
  11. 11.
    Kumar V (2013) Optimization and modeling of process parameters involved in ultrasonic machining of glass using design of experiments and regression approach. Am J Mater Eng Technol 1(1):13–18MathSciNetGoogle Scholar
  12. 12.
    Chakravorty R, Gauri SK, Chakraborty S (2013) Optimization of multiple responses of ultrasonic machining (USM) process: a comparative. Int J Ind Eng Comput 4:285–296Google Scholar
  13. 13.
    Kumar J (2014) Investigations into the surface quality and micro-hardness in the ultrasonic machining of titanium (ASTM GRADE-1). J Brazilian Soc Mech Sci Eng 36(4):807–823MathSciNetCrossRefGoogle Scholar
  14. 14.
    Agarwal S (2015) On the mechanism and mechanics of material removal in ultrasonic machining. Int J Mach Tools Manuf 96(1):1–14CrossRefGoogle Scholar
  15. 15.
    Kuruc M, Vopát T, Peterka J (2015) Surface roughness of poly-crystalline cubic boron nitride after rotary ultrasonic machining. Procedia Eng 100:877–884CrossRefGoogle Scholar
  16. 16.
    Teimouri R, Baseri H, Moharami R (2015) Multi-responses optimization of ultrasonic machining process. J Intel Manuf 26(4):745–753CrossRefGoogle Scholar
  17. 17.
    Kuriakose S, Kumar P, Bhatt J (2017) Machinability study of Zr-Cu-Ti metallic glass by micro hole drilling using micro-USM. J Mater Process Technol 240:42–51CrossRefGoogle Scholar
  18. 18.
    Geng D, Teng Y, Liu Y, Shao Z, Jiang X, Zhang D (2019) Experimental study on drilling load and hole quality during rotary ultrasonic helical machining of small-diameter CFRP holes. J Mat Process Technol 270:195–205CrossRefGoogle Scholar
  19. 19.
    Yang X, Deb S (2010) Engineering optimization by Cuckoo search. Int J Math Model Numer Opt 1:330–343zbMATHGoogle Scholar
  20. 20.
    Rajabioun R (2011) Cuckoo optimization algorithm. App Soft Comput 11:5508–5518CrossRefGoogle Scholar
  21. 21.
    Valian E, Tavakoli S, Mohanna S, Haghi A (2013) Improved cuckoo search for reliability optimization problem. Comput Ind Eng 64:459–468CrossRefGoogle Scholar
  22. 22.
    Chapman SJ (2008) Matab® programming for engineers. Thomson Asia Ltd, SingaporeGoogle Scholar
  23. 23.
    Hahn BH, Valentine DT (2010) Essential matlab for engineers and scientist. Elsevier Academic PressGoogle Scholar
  24. 24.
    Korkut I, Acır A, Boy M (2011) Application of regression and artificial neural network analysis in modelling of tool–chip interface temperature in machining. Expert Sys Appl 38(9):11651–11656CrossRefGoogle Scholar
  25. 25.
    Kumar M, Rawat TK (2015) Optimal design of FIR fractional order differentiator using cuckoo search algorithm. Expert Sys Appl 42:3433–3449CrossRefGoogle Scholar
  26. 26.
    Rajabi-Bahaabadi M, Shariat-Mohaymany A, Babaei M, Ahn CW (2015) Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm. Expert Sys Appl 42(12):5056–5064CrossRefGoogle Scholar
  27. 27.
    De Oliveira LW, Carlos Campos Rubio J, Gilberto Duduch J, De Almeida PEM (2015) Correcting geometric deviations of CNC machine-tools: an approach with artificial neural networks. App Soft Comput J 36:114–124CrossRefGoogle Scholar
  28. 28.
    Huang J, Gao L, Li X (2015) An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. App Soft Comput J 36:349–356CrossRefGoogle Scholar
  29. 29.
    Binh HT, Hanh NT, Dey N (2018) Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl 30(7):2305–2317CrossRefGoogle Scholar
  30. 30.
    Li Z, Dey N, Ashour AS, Tang Q (2018) Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem. Neural Comput Appl 30(9):2685–2696CrossRefGoogle Scholar
  31. 31.
    Chakraborty S, Dey N, Samanta S, Ashour AS, Barna C, Balas MM (2017) Optimization of non-rigid demons registration using a cuckoo search algorithm. Cognit Comput 9(6):817–826CrossRefGoogle Scholar
  32. 32.
    Zhu X, Wang N (2019). Cuckoo search algorithm with onlooker bee search for modeling PEMFCs using T2FNN, Engi Appl Artif Intel, 85., 740–753
  33. 33.
    Lalchhuanvela H, Doloi B, Bhattacharyya B (2012) Enabling and understanding ultrasonic machining of engineering ceramics using parametric analysis. Mat Manuf Process 27(4):443–448CrossRefGoogle Scholar
  34. 34.
    Li S, Wan B, Landers RG (2014) Surface roughness optimization in processing SiC mono-crystal wafers by wire saw machining with ultrasonic vibration. Proc IMechE Part B: J Engi Manuf 228(5):725–739CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentSardar Vallabhbhai National Institute of TechnologySuratIndia

Personalised recommendations