Surface Roughness Modelling and Prediction Using Artificial Intelligence Based Models

  • Musa Alhaji IbrahimEmail author
  • Yusuf Şahin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)


Surface roughness is a significant factor in product quality. Experimental investigation of surface roughness is associated with cumbersomeness, high cost and energy consumption. The results of artificial intelligence (AI) based models namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) were presented. Empirical data of hard turning operations containing cutting speed, feed rate and depth of cut and average surface roughness (Ra) were used as inputs and target process parameters respectively to train the AI based models. The performances of the models were evaluated using determination coefficient (DC) and root mean square error (RMSE) criteria. The results of the one-process-parameter influence on Ra donated that both models showed that feed rate was the most influential and dominant process parameter on Ra. However, the sensitivity analysis results indicated that the two different AI based models behaved differently with ANN having cutting speed and depth of cut as the most influential and dominant input-combination process parameters while that of ANFIS showed that feed rate and depth of cut were while describing and predicting the same surface roughness process under the same conditions. The results of the models were in concord with the empirical results of the average surface roughness. Both models predicted Ra but ANN performed better than ANFIS in both scenarios. The models can be used to design for the average surface roughness of hardened steel in hard turning operation thus saving cost, time and energy consumption in the experimental determination of average surface roughness, Ra.


ANN ANFIS Surface roughness 


  1. 1.
    Özel, K.Y.T.: Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int. J. Mach. Tools Manuf. 45(4–5), 467–479 (2005)CrossRefGoogle Scholar
  2. 2.
    Sofuo, A., Orak, S.: An ANN-based method to predict surface roughness in turning operations. Arab. J. Sci. Eng. 42, 1929–1940 (2017)CrossRefGoogle Scholar
  3. 3.
    Kwon, Y.J., Tseng, T.-L.B., Konada, U.: A novel approach to predict surface roughness in machining operations using fuzzy set theory. J. Comput. Des. Eng. 3, 1–13 (2016)Google Scholar
  4. 4.
    Laouissi, A., Yallese, M.A., Belbah, A., Belhadi, S., Haddad, A.: Investigation, modeling, and optimization of cutting parameters in turning of gray cast iron using coated and uncoated silicon nitride ceramic tools Based on ANN, RSM, and GA optimization. Int. J. Adv. Manuf. Technol. 101, 523–548 (2019)CrossRefGoogle Scholar
  5. 5.
    Rajeev, D., Dinakaran, D., Lead, G., Muthuraman, S.: Prediction of roughness in hard turning of AISI 4140 steel through artifical neural network and regression models. Int. J. Mech. Eng. Technol. 7(5), 200–208 (2016)Google Scholar
  6. 6.
    Hossain, A.N.: Surface roughness prediction modelling for commercial dies using ANFIS, ANN and RSM. Int. J. Ind. Syst. Eng. 16(2), 156–183 (2014)Google Scholar
  7. 7.
    Kamruzzaman, M., Rahman, S.S., Ashraf, M.Z.I., Dhar, N.R.: Modeling of chip–tool interface temperature using response surface methodology and artificial neural network in HPC-assisted turning and tool life investigation. Int. J. Adv. Manuf. Technol. 90(5–8), 1547–1568 (2017)CrossRefGoogle Scholar
  8. 8.
    Sarma, D.K., Dixit, U.S.: A comparison of dry and air-cooled turning of grey cast iron with mixed oxide ceramic tool. J. Mater. Process. Technol. 190, 160–172 (2007)CrossRefGoogle Scholar
  9. 9.
    Karayel, D.: Prediction and control of surface roughness in CNC lathe using artificial neural network. J. Mater. Process. Technol. 209, 3125–3137 (2009)CrossRefGoogle Scholar
  10. 10.
    Ho, S.J., Lee, S.Y., Chen, K.C., Ho, S.S.: Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro fuzzy inference system. Int. J. Mach. Tools Manuf. 42, 1441–1446 (2002)CrossRefGoogle Scholar
  11. 11.
    Davim, S.R., Gaitonde, J.P., Karnikc, V.N.: Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. J. Mater. Process. Technol. 205, 16–23 (2008)CrossRefGoogle Scholar
  12. 12.
    Zhong, Z.W., Khoo, L.P., Han, S.T.: Prediction of surface roughness of turned surfaces using neural networks. Int. J. Adv. Manuf. Technol. 28(7–8), 688–693 (2006)CrossRefGoogle Scholar
  13. 13.
    Sahin, Y., Motorcu, A.R.: Surface roughness model in machining hardened steel with cubic boron nitride cutting tool. Int. J. Refract. Met. Hard Mater. 26, 84–90 (2008)CrossRefGoogle Scholar
  14. 14.
    Bouacha, K., Athmane, M., Mabrouki, T., Rigal, J.: Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. Int. J. Refract. Met. Hard Mater. 28(3), 349–361 (2010)CrossRefGoogle Scholar
  15. 15.
    Sahin, Y., Motorcu, A.R.: The develpoment of surface roughness model when turning hardened steel with ceramic cutting tool using response methodology. Multidiscip. Model. Mater. Struct. 4, 290–304 (2008)Google Scholar
  16. 16.
    Akkus, H.: Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement 44, 1697–1704 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Near East UniversityNicosiaCyprus
  2. 2.Kano University of Science and TechnologyWudilNigeria

Personalised recommendations