Skip to main content
SpringerLink
Log in

Hybrid approach for modelling and optimizing MQL grinding of Inconel 625 with machine learning and MCDM techniques

  • Original Article
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

Abstract

This study aims to optimize the process of minimum quantity lubrication (MQL) grinding for Inconel 625 (IN 625) while enhancing its comprehensibility. The research employed experimental methods based on the Box-Behnken design to investigate critical parameters, including tangential force, surface roughness, specific energy, and apparent coefficient of friction. Further, machine learning techniques, specifically Random forest regression and Gaussian process regression (GPR), have been employed to build predictive models. These models have been assessed using metrics like R2, mean absolute error, and root mean square error. The results demonstrate that GPR outperforms other techniques in predicting the data accurately. Additionally, this study utilized multi-criteria decision-making techniques, namely TOPSIS and VIKOR, in conjunction with the entropy method to determine the optimal conditions for MQL grinding of IN 625. The optimized parameters for achieving low tangential force, high surface roughness, low specific energy, and low apparent coefficient of friction have been identified as a wheel speed of 1800 m/min, table speed of 9000 mm/min, and a depth of cut of 0.01 mm. Furthermore, a higher value of Ra (surface roughness) indicates the superior effectiveness of MQL grinding compared to dry grinding, as applying the MQL technique helps retain the sharpness of grit for a longer period of time. SEM image and EDS analysis of the ground surfaces confirm that better surface morphology has been obtained at optimized parameters of MQL grinding.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Data will be made available on request.

Abbreviations

ML:

Machine learning

MCDM:

Multicriteria decision-making

TOPSIS:

Technique for order of preference by similarity to ideal solution

ACoF:

Apparent coefficient of friction

SPE:

Specific energy

IN 625:

Inconel 625

MAE:

Mean absolute error

RMSE:

Root mean square error

RFR:

Random forest regression

GPR:

Gaussian process regression

MLP:

Multilayer perceptrons

SVM:

Support vector machine

KNN:

Kth-nearest neighbour

References

  1. Lv, L., Deng, Z., Liu, T., Li, Z., Liu, W.: Intelligent technology in grinding process driven by data: a review. J. Manuf. Process. 58, 1039–1051 (2020). https://doi.org/10.1016/j.jmapro.2020.09.018

    Article  Google Scholar 

  2. Manikandan, N., Thejasree, P., Lakshmi Narasimhamu, K., Krishnamachary, P.C.: Investigations on machinability and evolution of hybrid artificial intelligent tools for contemporary machining of nickel alloy. Mater. Manuf. Process. (2022). https://doi.org/10.1080/10426914.2022.2157430

    Article  Google Scholar 

  3. Bonaccorso, G.: Machine Learning Algorithms. Packt Publishing Ltd, Birmingham (2017)

    Google Scholar 

  4. Klocke, F., Kuchle, A.: Manufacturing Processes, vol. 2. Springer, Berlin (2009)

    Google Scholar 

  5. Rakesh, P.R., Chakradhar, D.: Experimental investigation of hybrid cooling approach using a modified tool holder for turning of Inconel 625 superalloy. Int. J. Adv. Manuf. Technol. (2023). https://doi.org/10.1007/s00170-022-10781-4

    Article  Google Scholar 

  6. Awale, A.S., Chaudhari, A., Kumar, A., Khan Yusufzai, M.Z., Vashista, M.: Synergistic impact of eco-friendly nano-lubricants on the grindability of AISI H13 tool steel: a study towards clean manufacturing. J. Clean. Prod. 364, 132 (2022)

    Article  Google Scholar 

  7. Malik, A.K., Ghosh, S., Pandey R.K.: Experimental studies on the grinding of Ti–6Al–4V using micro and nano size solid lubricants. In: All India Manufacturing Technology, dEsign and Research Conference-2014, pp. 3–7 (2014)

  8. Brian Rowe, P.W., Li Yan, I., Inasaki, Malkin, S.: Applications of artificial intelligence in grinding. CIRP Ann. 43(2), 521–531 (1994)

    Article  Google Scholar 

  9. Prashanth, G.S., Sekar, P., Bontha, S., Balan, A.S.S.: Grinding parameters prediction under different cooling environments using machine learning techniques. Mater. Manuf. Process. 38(2), 235–244 (2023)

    Article  CAS  Google Scholar 

  10. Gao, K., Chen, H., Zhang, X., Ren, X.K., Chen, J., Chen, X.: A novel material removal prediction method based on acoustic sensing and ensemble XG Boost learning algorithm for robotic belt grinding of Inconel 718. Int. J. Adv. Manuf. Technol. 105(1–4), 217–232 (2019)

    Article  Google Scholar 

  11. Pandiyan, V., Caesarendra, W., Glowacz, A., Tjahjowidodo, T.: Modelling of material removal in abrasive belt grinding process: a regression approach. Symmetry (Basel) 12(1), 9 (2020). https://doi.org/10.3390/SYM12010099

    Article  Google Scholar 

  12. Jha, P., et al.: A hybrid ensemble learning model for evaluating the surface roughness of AZ91 alloy during the end milling operation. Surf. Rev. Lett. (2023). https://doi.org/10.1142/S0218625X23400048

    Article  Google Scholar 

  13. Kumar, V., Choudhary, A.K.: A hybrid response surface methodology and multi-criteria decision making model to investigate the performance and emission characteristics of a diesel engine fueled with phenolic antioxidant additive and biodiesel blends. J. Energy Resour. Technol. 145(9), 092302 (2023)

    Article  CAS  Google Scholar 

  14. Dinbandhu, Abhishek, K.: Parametric optimization and evaluation of RMDTM welding performance for ASTM A387 Grade 11 steel plates using TOPSIS-Taguchi approach. In: International Conference on Advances in Materials Processing & Manufacturing Applications, pp. 215–227 (2020)

  15. Chakraborty, S., Chakraborty, S.: A scoping review on the applications of MCDM techniques for parametric optimization of machining processes. Arch. Comput. Methods Eng. (2022). https://doi.org/10.1007/s11831-022-09731-w

    Article  PubMed  PubMed Central  Google Scholar 

  16. Maity, S.R., Chakraborty, S.: Grinding wheel abrasive material selection using fuzzy TOPSIS method. Mater. Manuf. Process. 28(4), 408–417 (2013). https://doi.org/10.1080/10426914.2012.700159

    Article  CAS  Google Scholar 

  17. Awale, A.S., Vashista, M., Khan Yusufzai, M.Z.: Multi-objective optimization of MQL mist parameters for eco-friendly grinding. J. Manuf. Process. 56, 75–86 (2020). https://doi.org/10.1016/j.jmapro.2020.04.069

    Article  Google Scholar 

  18. Huy, T.Q., et al.: Application of TOPSIS, MAIRCA and EAMR methods for multi-criteria decision making in cubic boron nitride grinding. East. Eur. J. Enterp. Technol. 3(1–117), 58–66 (2022)

    Google Scholar 

  19. Khan, A.M., et al.: Multi-objective optimization for grinding of AISI D2 steel with Al2O3 wheel under MQL. Materials (2018). https://doi.org/10.3390/ma11112269

    Article  PubMed  PubMed Central  Google Scholar 

  20. Stephen, D.S., Sethuramalingam, P.: Optimization of grinding titanium with 2%CNT-CBN wheel using TOPSIS. Mater. Manuf. Process. 37(14), 1679–1690 (2022). https://doi.org/10.1080/10426914.2022.2039696

    Article  CAS  Google Scholar 

  21. Liao, T.W., Hua, G., Qu, J., Blau, P.J.: Grinding wheel condition monitoring with Hidden Markov model-based clustering methods. Mach. Sci. Technol. 10(4), 511–538 (2006). https://doi.org/10.1080/10910340600996175

    Article  Google Scholar 

  22. Li, C., Jiao, F., Ma, X., Niu, Y., Tong, J.: Dressing principle and parameter optimization of ultrasonic-assisted diamond roller dressing WA grinding wheel using response surface methodology and genetic algorithm. Int. J. Adv. Manuf. Technol. (2023). https://doi.org/10.1007/s00170-023-11916-x

    Article  Google Scholar 

  23. Patnaik, L., Maity, S.R., Kumar, S.: Evaluation of gamma irradiated Ti6Al4V and silver alloyed aC coatings as friction pair via response surface methodology. Adv. Mater. Process. Technol. 8(sup3), 1456–1473 (2022)

    Google Scholar 

  24. Sinha, M.K., Setti, D., Ghosh, S., Rao, P.V.: An alternate method for optimisation of minimum quantity lubrication parameters in surface grinding. Int. J. Mach. Mach. Mater. 18(5–6), 586–605 (2016)

    Google Scholar 

  25. Grömping, U.: Variable importance assessment in regression: linear regression versus random forest. Am. Stat. 63(4), 308–319 (2009). https://doi.org/10.1198/tast.2009.08199

    Article  MathSciNet  Google Scholar 

  26. Hultquist, C., Chen, G., Zhao, K.: A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests. Remote Sens. Lett. 5(8), 723–732 (2014). https://doi.org/10.1080/2150704X.2014.963733

    Article  Google Scholar 

  27. Schulz, E., Speekenbrink, M., Krause, A.: A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions. J. Math. Psychol. 85, 1–16 (2018). https://doi.org/10.1016/j.jmp.2018.03.001

    Article  MathSciNet  Google Scholar 

  28. Kumar, R., et al.: Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: a critical review. J. Mater. Res. Technol. 10, 1471–1492 (2021)

    Article  Google Scholar 

  29. Yuvaraj, N., Pradeep Kumar, M.: Multiresponse optimization of abrasive water jet cutting process parameters using TOPSIS approach. Mater. Manuf. Process. 30(7), 882–889 (2015). https://doi.org/10.1080/10426914.2014.994763

    Article  CAS  Google Scholar 

  30. Opricovic, S., Tzeng, G.H.: Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res.Oper. Res. 156(2), 445–455 (2004). https://doi.org/10.1016/S0377-2217(03)00020-1

    Article  Google Scholar 

  31. Madarkar, R., Agarwal, S., Attar, P., Ghosh, S., Rao, P.V.: Application of ultrasonic vibration assisted MQL in grinding of Ti–6Al–4V. Mat. Manuf. Process. 33(13), 1445–1452 (2018)

    Article  CAS  Google Scholar 

  32. Balan, A.S.S., Vijayaraghavan, L., Krishnamurthy, R., Kuppan, P., Oyyaravelu, R.: An experimental assessment on the performance of different lubrication techniques in grinding of Inconel 751. J. Adv. Res. 7(5), 709–718 (2016). https://doi.org/10.1016/j.jare.2016.08.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hemdan, E.E.D., El-Shafai, W., Sayed, A.: CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications. J. Ambient. Intell. Humaniz. Comput.Intell. Humaniz. Comput. 14(9), 11715–11727 (2023). https://doi.org/10.1007/s12652-022-03732-0

    Article  Google Scholar 

  34. Khoshi, A., Shams Gooshki, H., Mahmoudi, N.: The data on the effective qualifications of teachers in medical sciences: an application of combined fuzzy AHP and fuzzy TOPSIS methods. Data Brief 21, 2689–2693 (2018). https://doi.org/10.1016/j.dib.2018.10.165

    Article  PubMed  PubMed Central  Google Scholar 

  35. Zeng, Q.L., Li, D.D., Bin Yang, Y.: VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management. J. Med. Syst. (2013). https://doi.org/10.1007/s10916-012-9908-1

    Article  PubMed  Google Scholar 

  36. Awale, A.S., Srivastava, A., Vashista, M., Khan Yusufzai, M.Z.: Influence of minimum quantity lubrication on surface integrity of ground hardened H13 hot die steel. Int. J. Adv. Manuf. Technol. 100(1–4), 983–997 (2019). https://doi.org/10.1007/s00170-018-2777-0

    Article  Google Scholar 

  37. Awale, A.S., Vashista, M., Khan Yusufzai, M.Z.: Application of eco-friendly lubricants in sustainable grinding of die steel. Mater. Manuf. Process. 36(6), 702–712 (2021). https://doi.org/10.1080/10426914.2020.1866187

    Article  CAS  Google Scholar 

Download references

Funding

The author(s) received no financial support for this article's research, authorship and/or publication.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, 136119, India

    Manoj Kumar Sinha & Ranjeet Kumar

  2. Department of Mechanical Engineering, National Institute of Technology Hamirpur, Hamirpur, HP, 177005, India

    Kamal Kishore

  3. Jindal Global Business School Sonipat, O. P. Jindal Global University Sonipat, Sonipat, Haryana, 131001, India

    Archana

Authors
  1. Manoj Kumar Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamal Kishore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Archana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ranjeet Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj Kumar Sinha.

Ethics declarations

Conflict of interest

The author(s) declared no potential conflicts of interest concerning this article's research, authorship, and/or publication.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sinha, M.K., Kishore, K., Archana et al. Hybrid approach for modelling and optimizing MQL grinding of Inconel 625 with machine learning and MCDM techniques. Int J Interact Des Manuf (2024). https://doi.org/10.1007/s12008-024-01738-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12008-024-01738-w

Keywords

Search

Navigation