Advertisement

A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060

  • Rajneesh Kumar Singh
  • Swati Gangwar
  • D. K. Singh
  • Vimal Kumar PathakEmail author
Technical Paper
  • 16 Downloads

Abstract

In industries, the impact of magnetic abrasive finishing (MAF) is well recognized in achieving accurate surfaces, minimizing imperfections and providing high-quality surface finish especially in micro- and nano-range. In the same context, this paper presents a hybrid optimization method for multi-objective optimization that combines back-propagation artificial neural network (ANN) with a newly developed nature-inspired optimization algorithm, i.e. moth-flame optimization (MFO) algorithm, which is used to predict optimal process parameters of magnetic abrasive finishing process. The back-propagation ANN is gradient-based local search algorithm, while MFO is a rapid and global search algorithm for controlling the generation of candidate solutions. Initially, the MFO algorithm is integrated for training the neural network with unknown set of weights and biases and in the process overcomes the shortcomings of traditional training algorithms having slow convergence and local optima stagnation. Further, the MFO algorithm is also employed to predict the number of neurons in hidden layers. Finally, the proposed hybrid ANN–MFO methodology establishes a multi-objective optimization model in terms of important process parameters for optimizing the MAF process conditions. The optimization of different MAF process parameters is performed using MFO algorithm by incorporating the adaptive search procedure, which further enhances the exploration behaviour. Surface roughness, temperature of workpiece during the finishing operation and hardness of finished surface are investigated as the output response in the finishing of aluminium 6060. The design variables considered are working gap, abrasive weight, voltage and rotational speed. The results predicted by the proposed hybrid ANN–MFO algorithm provide effective and accurate process parameters to enhance surface finish and part quality that will be beneficial for real manufacturing environment.

Keywords

Surface quality Hardness ANN Optimization Magnetic Abrasive Finishing 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Singh DK, Jain VK, Raghuram V (2006) Experimental investigations into forces acting during a magnetic abrasive finishing process. Int J Adv Manuf Technol 30(7–8):652–662CrossRefGoogle Scholar
  2. 2.
    Kim TW, Kang DM, Kwak JS (2010) Application of magnetic abrasive polishing to composite materials. J Mech Sci Technol 24(5):1029–1034CrossRefGoogle Scholar
  3. 3.
    Fox M, Agrawal K, Shinmura T, Komanduri R (1994) Magnetic abrasive finishing of rollers. CIRP Ann 43(1):181–184CrossRefGoogle Scholar
  4. 4.
    Singh DK, Jain VK, Raghuram V, Komanduri R (2005) Analysis of surface texture generated by a flexible magnetic abrasive brush. Wear 259(7–12):1254–1261CrossRefGoogle Scholar
  5. 5.
    Hung CL, Ku WL, Yang LD (2010) Prediction system of magnetic abrasive finishing (MAF) on the internal surface of a cylindrical tube. Mater Manuf Process 25(12):1404–1412CrossRefGoogle Scholar
  6. 6.
    Jain VK, Kumar P, Behera PK, Jayswal SC (2001) Effect of working gap and circumferential speed on the performance of magnetic abrasive finishing process. Wear 250(1–12):384–390CrossRefGoogle Scholar
  7. 7.
    Kim JD (2003) Polishing of ultra-clean inner surfaces using magnetic force. Int J Adv Manuf Technol 21(2):91–97CrossRefGoogle Scholar
  8. 8.
    Misra A, Pandey PM, Dixit US (2017) Modeling of material removal in ultrasonic assisted magnetic abrasive finishing process. Int J Mech Sci 131:853–867CrossRefGoogle Scholar
  9. 9.
    Yin S, Shinmura T (2004) A comparative study: polishing characteristics and its mechanisms of three vibration modes in vibration-assisted magnetic abrasive polishing. Int J Mach Tools Manuf 44(4):383–390CrossRefGoogle Scholar
  10. 10.
    Mulik RS, Pandey PM (2011) Magnetic abrasive finishing of hardened AISI 52100 steel. Int J Adv Manuf Technol 55(5–8):501–515CrossRefGoogle Scholar
  11. 11.
    Kariem Shather S, Mousa SM (2015) The influence of design and technological parameters on the MAF process. Al-Khwarizmi Eng J 11(4):82–88Google Scholar
  12. 12.
    Mulik RS, Srivastava V, Pandey PM (2012) Experimental investigations and modeling of temperature in the work-brush interface during ultrasonic assisted magnetic abrasive finishing process. Mater Manuf Process 27(1):1–9CrossRefGoogle Scholar
  13. 13.
    Mulik RS, Pandey PM (2011) Magnetic abrasive finishing of hardened AISI 52100 steel. Int J Adv Manuf Technol 55(5–8):501–515CrossRefGoogle Scholar
  14. 14.
    Lee YH, Wu KL, Jhou JH, Tsai YH, Yan BH (2013) Two-dimensional vibration-assisted magnetic abrasive finishing of stainless steel SUS304. Int J Adv Manuf Technol 69(9–12):2723–2733CrossRefGoogle Scholar
  15. 15.
    Yang LD, Lin CT, Chow HM (2009) Optimization in MAF operations using Taguchi parameter design for AISI304 stainless steel. Int J Adv Manuf Technol 42(5–6):595CrossRefGoogle Scholar
  16. 16.
    Teimouri R, Baseri H (2013) Artificial evolutionary approaches to produce smoother surface in magnetic abrasive finishing of hardened AISI 52100 steel. J Mech Sci Technol 27(2):533–539CrossRefGoogle Scholar
  17. 17.
    Judal KB, Yadava V (2013) Electrochemical magnetic abrasive machining of AISI304 stainless steel tubes. Int J Precis Eng Manuf 14(1):37–43CrossRefGoogle Scholar
  18. 18.
    Vahdati M, Rasouli SA (2016) Study of magnetic abrasive finishing on freeform surface. Trans IMF 94(6):294–302CrossRefGoogle Scholar
  19. 19.
    Wu J, Zou Y, Sugiyama H (2015) Study on ultra-precision magnetic abrasive finishing process using low frequency alternating magnetic field. J Magn Magn Mater 386:50–59CrossRefGoogle Scholar
  20. 20.
    Kang J, George A, Yamaguchi H (2012) High-speed internal finishing of capillary tubes by magnetic abrasive finishing. Procedia CIRP 1:414–418CrossRefGoogle Scholar
  21. 21.
    Ahmad S, Gangwar S, Yadav PC, Singh DK (2017) Optimization of process parameters affecting surface roughness in magnetic abrasive finishing process. Mater Manuf Processes 32(15):1723–1729CrossRefGoogle Scholar
  22. 22.
    Verma GC, Kala P, Pandey PM (2017) Experimental investigations into internal magnetic abrasive finishing of pipes. Int J Adv Manuf Technol 88(5–8):1657–1668CrossRefGoogle Scholar
  23. 23.
    Khattri K, Choudhary G, Bhuyan BK, Selokar A (2018) A review on parametric analysis of magnetic abrasive machining process. In: IOP conference series: materials science and engineering, vol. 330, no. 1. IOP Publishing, p 012105Google Scholar
  24. 24.
    Choopani Y, Razfar MR, Saraeian P, Farahnakian M (2016) Experimental investigation of external surface finishing of AISI 440C stainless steel cylinders using the magnetic abrasive finishing process. Int J Adv Manuf Technol 83(9–12):1811–1821CrossRefGoogle Scholar
  25. 25.
    Shanbhag VV, Naveen K, Balashanmugam N, Vinod P (2016) Modelling for evaluation of surface roughness in magnetic abrasive finishing of flat surfaces. Int J Precis Technol 6(2):159–170CrossRefGoogle Scholar
  26. 26.
    Gopal PM, Prakash KS (2018) Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC. Measurement 116:178–192CrossRefGoogle Scholar
  27. 27.
    Hou ZB, Komanduri R (1998) Magnetic field assisted finishing of ceramics—part I: thermal model. J Tribol 120(4):645–651CrossRefGoogle Scholar
  28. 28.
    Kumar G, Yadav V (2009) Temperature distribution in the workpiece due to plane magnetic abrasive finishing using FEM. Int J Adv Manuf Technol 41(11–12):1051–1058CrossRefGoogle Scholar
  29. 29.
    Misra A, Pandey PM, Dixit US, Roy A, Silberschmidt VV (2018) Multi-objective optimization of ultrasonic-assisted magnetic abrasive finishing process. Int J Adv Manuf Technol 101(5–8):1661–1670Google Scholar
  30. 30.
    Oh JH, Lee SH (2011) Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusion. Proc Inst Mech Eng Part B J Eng Manuf 225(6):853–865CrossRefGoogle Scholar
  31. 31.
    Wang L, Zeng Y, Chen T (2015) Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst Appl 42(2):855–863CrossRefGoogle Scholar
  32. 32.
    Kim JS, Jung S (2015) Implementation of the RBF neural chip with the back-propagation algorithm for on-line learning. Appl Soft Comput 29:233–244CrossRefGoogle Scholar
  33. 33.
    Sexton RS, Gupta JN (2000) Comparative evaluation of genetic algorithm and backpropagation for training neural networks. Inf Sci 129(1–4):45–59CrossRefGoogle Scholar
  34. 34.
    Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249CrossRefGoogle Scholar
  35. 35.
    Gurney K (2014) An introduction to neural networks. CRC Press, LondonCrossRefGoogle Scholar
  36. 36.
    Blake C (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html. Accessed 25 Dec 2018
  37. 37.
    Wdaa ASI, Sttar A (2008) Differential evolution for neural networks learning enhancement (Doctoral dissertation, Universiti Teknologi Malaysia)Google Scholar
  38. 38.
    Pathak VK, Singh AK (2017) Accuracy control of contactless laser sensor system using whale optimization algorithm and moth-flame optimization. tm-Technisches Messen 84(11):734–746CrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  • Rajneesh Kumar Singh
    • 1
  • Swati Gangwar
    • 1
  • D. K. Singh
    • 1
  • Vimal Kumar Pathak
    • 2
    Email author
  1. 1.Department of Mechanical EngineeringMadan Mohan Malaviya University of TechnologyGorakhpurIndia
  2. 2.Department of Mechanical EngineeringManipal University JaipurJaipurIndia

Personalised recommendations