Journal of Intelligent Manufacturing

, Volume 30, Issue 2, pp 833–854 | Cite as

Working parameter optimization of strengthen waterjet grinding with the orthogonal-experiment-design-based ANFIS

  • Zhongwei LiangEmail author
  • Shaopeng Liao
  • Yiheng Wen
  • Xiaochu Liu


In this paper, the working parameter optimization of strengthen waterjet grinding by employing the orthogonal-experiment-design-based ANFIS (Adaptive Neural Fuzzy Inference System), was conducted to obtain an optimal result of bearing ring machining. An improved ANFIS system based upon orthogonal experiment design, was proposed to optimize the working parameters in grinding practices, which increases the surface hardness of ring surface from 49.0 to 72.0 HRC, topography elasticity variance from 330.0 to 670.0, texture energy from 24.5 to 88.0, decreases the surface roughness from 0.65 to 0.25 \(\upmu \)m, and loading deviation from 1860.5 to 1320.0, thereafter an optimal grinding quality can be obtained. The optimization approach proposed involve the following steps: Preparation of experimental environment; Measure index determination for ring surface; Orthogonal experiment design for making fuzzy logic rules; Establishment of ANFIS system; Working parameter optimization for waterjet grinding; and Performance verification for actual grinding. Objective of this research is determining the optimal working parameters with fewer experimental iterations compared to other alternative approaches, such as Genetic parameter optimization, SA–GA parametric prediction, Taguchi parameter estimation, ANN–SA parametric selection, and GONNs parameter selection method. Statistical analysis and result comparisons support its efficiency and reliability in machining practices, a stable and reliable grinding process can be achieved for typical conditions by using waterjet pressure at around 310ṀPa, flow rates of water mass at about 5.8 kg/min, attack angle by 60–75\({^{\circ }}\), mass rate of abrasive grit by about 0.4 kg/min, and traverse speed by 60 mm/min. It was concluded that this proposed ANFIS system can be used as a suitable and effective tool, to investigate the complicated influential correlation between waterjet working parameters and grinding effectiveness in bearing manufacturing, and to give a better machining performance compared to other experimental practices.


Working parameter Optimization Strengthen waterjet grinding Orthogonal experiment design ANFIS 



The author acknowledges the funding of following science foundations: the National Natural Science Foundation of China (51575116), the China National Spark Program (2015GA780065), the Innovative Academic Team Project of Guangzhou Education System (1201610013), the Science and Technology Planning Project of Guangdong Province (2016A010102022), the Science and Technology Planning Project of Guangzhou municipal government, the Water Resource Science and Technology Program of Guangdong Province of China (2012-11), the Postgraduate Education Innovation Program of Guangdong Province (2016XSLT24), The Foundation for Fostering the Scientific and Technical Innovation of Guangzhou University (GZHU[2016]-92), and The Key Integration Project of Industry, Education and Research of Guangzhou University were appreciated for supporting this work, the editors were thanked also for their hard work and the referees for their comments and valuable suggestions to improve this paper.

Compliance with ethical standards

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.


  1. Abdulshahed, A. M., Longstaff, A. P., & Fletcher, S. (2015a). The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Applied Soft Computing, 27(3), 158–168.Google Scholar
  2. Abdulshahed, A. M., Longstaff, A. P., Fletcher, S., & Myers, A. (2015b). Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Applied Mathematical Modelling, 39(7), 1837–1852.Google Scholar
  3. Abhishek, K., Panda, B. N., Datta, S., & Mahapatra, S. S. (2014). Comparing predictability of genetic programming and ANFIS on drilling performance modeling for GFRP composites. Procedia Materials Science, 6(5), 544–550.Google Scholar
  4. Al-Ghamdi, K., & Taylan, O. (2015). A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process. Computers & Industrial Engineering, 79(4), 27–41.Google Scholar
  5. Akhavan Niaki, F., Feng, L., Ulutan, D., & Mears, L. (2016). A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials. International Journal of Mechatronics and Manufacturing Systems, 9(2), 97–121.Google Scholar
  6. Axinte, D. A., Stepanian, J. P., Kong, M. C., & McGourlay, J. (2009). Abrasive waterjet turning–An efficient method to profile and dress grinding wheels. International Journal of Machine Tools and Manufacture, 49(3–4), 351–356.Google Scholar
  7. Azmi, A. I. (2015). Monitory of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites. Advances in Engineering Software, 82(3), 53–64.Google Scholar
  8. Bilbao Guillerna, A., Axinte, D., & Billingham, J. (2015). The linear inverse problem in energy beam processing with an application to abrasive waterjet machining. International Journal of Machine Tools and Manufacture, 99(4), 34–42.Google Scholar
  9. Çaydaş, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(3), 639–650.Google Scholar
  10. Chen, S., & Jiang, Z. (2015). A force controlled grinding-milling technique for quartz-glass micromachining. Journal of Materials Processing Technology, 216(4), 206–215.Google Scholar
  11. Dong, L., Sun, Y. D., & Li, D. J. (2010). Optimal deposition and layer modulation parameters for mechanical property enhancement of TiB\(_{2}\)/Si\(_{3}\)N\(_{4}\) multilayers using orthogonal experiment. Surface and Coatings Technology, 205(1), S422–S425.Google Scholar
  12. Fan, D., Ni, W., Yan, A., Wang, J., & Cui, W. (2015). Orthogonal experiments on direct reduction of carbon-bearing pellets of Bayer Red Mud. International Journal of Iron and Steel Research, 22(8), 686–693.Google Scholar
  13. Gajate, A., Haber, R., del Toro, R., Vega, P., & Bustillo, A. (2012). Tool wear monitoring using neuro-fuzzy techniques: A comparative study in a turning process. Journal of Intelligent Manufacturing, 23(3), 869–882.Google Scholar
  14. Gao, X., Zhang, Y., Zhang, H., & Qiong, W. (2012). Effects of machine tool configuration on its dynamics based on orthogonal experiment method. Chinese Journal of Aeronautics, 25(2), 285–291.Google Scholar
  15. Hayasi, M. T., & Asiabanpour, B. (2013). A new adaptive slicing approach for the fully dense freeform fabrication (FDFF) process. Journal of Intelligent Manufacturing, 24(4), 683–694.Google Scholar
  16. He, Z., Sun, Y., Zhang, G., Hong, Z., Xie, W., Xin, L., et al. (2015). Tribilogical performances of connecting rod and by using orthogonal experiment, regression method and response surface methodology. Applied Soft Computing, 29(3), 436–449.Google Scholar
  17. Jia, X., Guo, F., Huang, L., Salant, R. F., & Wang, Y. (2013). Parameter analysis of the radial lip seal by orthogonal array method. Tribology International, 64(2), 96–102.Google Scholar
  18. Labib, A. W., Keasberry, V. J., Atkinson, J., & Frost, H. W. (2011). Towards next generation electrochemical grinding controllers: A fuzzy logic control approach to ECM. Expert System on Application, 38(4), 7486–7493.Google Scholar
  19. Lee, Y., Filliben, J. J., Micheals, R. J., & Phillips, P. J. (2013). Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs. Computer Vision and Image Understanding, 117(5), 532–550.Google Scholar
  20. Liang, Z., Liu, X., & Tao, J. (2012). Fuzzy performance between surface fitting and energy distribution in turbulence runner. Scientific World Journal, 25(10), 100–113.Google Scholar
  21. Liang, Z., Liu, X., & Ye, B. (2013). Performance investigation of fitting algorithms in surface micro-topography grinding processes based on multi-dimensional fuzzy relation set. International Journal of Advanced Manufacturing Technology, 67(7), 2779–2798.Google Scholar
  22. Liang, Z., Liu, X., & Ye, B. (2014). Fuzzy evaluation of performance influence between surface fitting algorithms and turbulence kinetic energy distribution on runner section. Arabian Journal for Science and Engineering, 39(1), 2339–2351.Google Scholar
  23. Liang, Z., Liu, X., Zhou, J., & Liao, S. (2016). Video tracking for high-similarity drug tablets based on reflective energy intensity matrix and fuzzy recognition system. Proceedings of the Institution of Mechanical Engineers Part H: Journal of Engineering in Medicine, 230(3), 211–229.Google Scholar
  24. Liang, Z., Xie, B., Liao, S., & Zhou, J. (2015). Concentration degree prediction of AWJ grinding effectiveness based on turbulence Characteristics and the improved ANFIS. International Journal of Advanced Manufacturing Technology, 80(5), 887–905.Google Scholar
  25. Liang, Z. W., Ye, B. Y., & Wang, Y. J. (2012). Three-dimensional fuzzy influence analysis of fitting algorithms on integrated chip topographic modeling. Journal of Mechanical Science and Technology, 26(10), 3177–3191.Google Scholar
  26. Maher, I., Ling, L. H., Sarhan, A. A. D., & Hamdi, M. (2015). Improve wire EDM performance at different machining parameters–ANFIS modeling. IFAC-PapersOnLine, 48(1), 105–110.Google Scholar
  27. Mohamad, A., Zain, A. M., Bazin, N. E. N., & Udin, A. (2015). A process prediction model based on Cuckoo algorithm for abrasive waterjet machining. Journal of Intelligent Manufacturing, 26(6), 1247–1252.Google Scholar
  28. Muhammad, N., Manurung, Y. H. P., Jaafar, R., Abas, S. K., Tham, G., & Haruman, E. (2013). Model development for quality features of resistance spot welding using multi-objective Taguchi method and response surface methodology. Journal of Intelligent Manufacturing, 24(6), 1175–1183.Google Scholar
  29. Nagesh, S., Narasimha Murthy, H. N., Ratna Pal, M., & Krishna, B. S. S. (2015). Influence of nanofillers on the quality of CO\(_{2}\) laser drilling in vinylester/glass using orthogonal array experiments and grey relational analysis. Optics & Laser Technology, 69(5), 23–33.Google Scholar
  30. Nguyen, T., Shanmugam, D. K., & Wang, J. (2008). Effect of liquid properties on the stability of an abrasive waterjet. International Journal of Machine Tools and Manufacture, 48(10), 1138–1147.Google Scholar
  31. Odior, A. (2013). Application of neural network and fuzzy model to grinding process control. Evolving Systems, 4(3), 195–201.Google Scholar
  32. Phootrakornchai, W., & Jiriwibhakorn, S. (2015). Online critical clearing time estimation using an adaptive neuro-fuzzy inference system (ANFIS). International Journal of Electrical Power & Energy Systems, 73(2), 170–181.Google Scholar
  33. Prakash, S., Lilly Mercy, J., Teja, P. V. S., & Vijayalakshmi, P. (2014). ANFIS modeling of delamination during drilling of medium density fiber (MDF) board. Procedia Engineering, 97(5), 258–266.Google Scholar
  34. Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). A multi-performance prediction model based on ANFIS and new modified-GA for machining processes. Journal of Intelligent Manufacturing, 26(4), 703–716.Google Scholar
  35. Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). Robust optimization of ANFIS based on a new modified GA. Neurocomputing, 166(6), 357–366.Google Scholar
  36. Schwartzentruber, J., & Papini, M. (2015). Abrasive waterjet micro-piercing of borosilicate glass. Journal of Materials Processing Technology, 219(5), 143–154.Google Scholar
  37. Sedighi, M., & Afshari, D. (2010). Creep feed grinding optimization by an integrated GA-NN system. Journal of Intelligent Manufacturing, 21(6), 657–663.Google Scholar
  38. Sevil Ergur, H., & Oysal, Y. (2015). Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network. Journal of Intelligent Manufacturing, 26(2), 403–413.Google Scholar
  39. Shabgard, M. R., Badamchizadeh, M. A., Ranjbary, G., & Amini, K. (2013). Fuzzy approach to select grinding parameters in electrical discharge grinding (EDM) and ultrasonic-assisted EDM processes. Journal of Manufacture System, 32(5), 32–39.Google Scholar
  40. Srinivasu, D. S., & Axinte, D. A. (2014). Surface integrity analysis of plain waterjet milled advanced engineering composite materials. Procedia CIRP, 13(6), 371–376.Google Scholar
  41. Tangwarodomnukun, V., Wang, J., Huang, C. Z., & Zhu, H. T. (2014). Heating and material removal process in hybrid laser-waterjet ablation of silicon substrates. International Journal of Machine Tools and Manufacture, 79(4), 1–16.Google Scholar
  42. Teimouri, R., & Baseri, H. (2015). Forward and backward predictions of the friction stir welding parameters using fuzzy-artificial bee colony-imperialist competitive algorithm systems. Journal of Intelligent Manufacturing, 26(2), 307–319.Google Scholar
  43. Teimouri, R., Baseri, H., & Moharami, R. (2015). Multi-responses optimization of ultrasonic machining process. Journal of Intelligent Manufacturing, 26(4), 745–753.Google Scholar
  44. 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(6), 1463–1472.Google Scholar
  45. Zhang, T., Liu, X., Sun, F., & Zhang, Z. (2015). The deposition parameters in the synthesis of CVD microcrystalline diamond powders optimized by the orthogonal experiment. Journal of Crystal Growth, 426(5), 15–24.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zhongwei Liang
    • 1
    • 2
    Email author
  • Shaopeng Liao
    • 1
    • 2
  • Yiheng Wen
    • 1
    • 2
  • Xiaochu Liu
    • 1
    • 2
  1. 1.School of Mechanical and Electrical Engineering, Guangzhou UniversityGuangzhouPeople’s Republic of China
  2. 2.The Guangzhou Key Laboratory for Strengthen Grinding and High Performance Machining of Metal MaterialGuangzhou UniversityGuangzhouPeople’s Republic of China

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