Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

  • AmirMahyar Khorasani
  • Mohammad Reza Soleymani Yazdi


Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.


Graphical Abstract

A dynamic surface roughness monitoring system in milling operations of Al 7075 and St 52 is developed based on the ANNs using cutting conditions, vibrations in X and Z directions, and cutting fluid as inputs and surface roughness as output.


Artificial neural networks Milling Process Simulation Surface Roughness 


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  1. 1.
    Hedberg GK, Shin YC, Xu L (2015) Laser-assisted milling of Ti-6Al-4V with the consideration of surface integrity. Int J Adv Manuf Technol 79(9-12):1645–1658CrossRefGoogle Scholar
  2. 2.
    Imran M et al (2015) Assessment of surface integrity of Ni superalloy after electrical-discharge, laser and mechanical micro-drilling processes. Int J Adv Manuf Technol 79(5-8):1303–1311CrossRefGoogle Scholar
  3. 3.
    Parenti P et al (2015) A model-based approach for online estimation of surface waviness in roll grinding. Int J Adv Manuf Technol 79(5-8):1195–1208CrossRefGoogle Scholar
  4. 4.
    Wang T, Xie L, Wang X (2015) Simulation study on defect formation mechanism of the machined surface in milling of high volume fraction SiCp/Al composite. Int J Adv Manuf Technol 79(5-8):1185–1194CrossRefGoogle Scholar
  5. 5.
    Zhao Q et al (2015) Tool life and hole surface integrity studies for hole-making of Ti6Al4V alloy. Int J Adv Manuf Technol 79(5-8):1017–1026CrossRefGoogle Scholar
  6. 6.
    Altug M, Erdem M, Ozay C (2015) Experimental investigation of kerf of Ti6Al4V exposed to different heat treatment processes in WEDM and optimization of parameters using genetic algorithm. Int J Adv Manuf Technol 78(9-12):1573–1583CrossRefGoogle Scholar
  7. 7.
    Ghobadi S et al (2015) Developing a fuzzy multivariate CUSUM control chart to monitor multinomial linguistic quality characteristics. Int J Adv Manuf Technol 79(9-12):1893–1903CrossRefGoogle Scholar
  8. 8.
    Khorasani AM, Aghchai AJ, Khorram A (2011) Chatter prediction in turning process of conical workpieces by using case-based resoning (CBR) method and Taguchi design of experiment. Int J Adv Manuf Technol 55(5-8):457–464CrossRefGoogle Scholar
  9. 9.
    Oktem H, Erzurumlu T, Erzincanli F (2006) Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater Des 27(9):735–744CrossRefGoogle Scholar
  10. 10.
    Liu T-I, Jolley B (2015) Tool condition monitoring (TCM) using neural networks. Int J Adv Manuf Technol 78(9-12):1999–2007CrossRefGoogle Scholar
  11. 11.
    Elgargni M, Al-Habaibeh A, Lotfi A (2015) Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks. Int J Adv Manuf Technol 77(9-12):1965–1978CrossRefGoogle Scholar
  12. 12.
    Gupta A et al (2015) Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks. Int J Adv Manuf Technol 77(1-4):331–339CrossRefGoogle Scholar
  13. 13.
    Benardos P, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput Integr Manuf 18(5):343–354CrossRefGoogle Scholar
  14. 14.
    Risbood K, Dixit U, Sahasrabudhe A (2003) Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132(1):203–214CrossRefGoogle Scholar
  15. 15.
    Zain AM, Haron H, Sharif S (2010) Prediction of surface roughness in the end milling machining using artificial neural network. Expert Syst Appl 37(2):1755–1768CrossRefGoogle Scholar
  16. 16.
    Özel T, Hsu T-K, Zeren E (2005) Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel. Int J Adv Manuf Technol 25(3-4):262–269CrossRefGoogle Scholar
  17. 17.
    Huang BP, Chen JC, Li Y (2008) Artificial-neural-networks-based surface roughness Pokayoke system for end-milling operations. Neurocomputing 71(4):544–549CrossRefGoogle Scholar
  18. 18.
    Topal ES (2009) The role of stepover ratio in prediction of surface roughness in flat end milling. Int J Mech Sci 51(11):782–789CrossRefGoogle Scholar
  19. 19.
    Ezugwu E et al (2005) Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int J Mach Tools Manuf 45(12):1375–1385CrossRefGoogle Scholar
  20. 20.
    Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45(4):467–479CrossRefGoogle Scholar
  21. 21.
    Lalwani D, Mehta N, Jain P (2008) Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. J Mater Process Technol 206(1):167–179CrossRefGoogle Scholar
  22. 22.
    Brezocnik M, Kovacic M, Ficko M (2004) Prediction of surface roughness with genetic programming. J Mater Process Technol 157:28–36CrossRefGoogle Scholar
  23. 23.
    Baek DK, Ko TJ, Kim HS (2001) Optimization of feedrate in a face milling operation using a surface roughness model. Int J Mach Tools Manuf 41(3):451–462CrossRefGoogle Scholar
  24. 24.
    Öktem H, Erzurumlu T, Kurtaran H (2005) Application of response surface methodology in the optimization of cutting conditions for surface roughness. J Mater Process Technol 170(1):11–16CrossRefGoogle Scholar
  25. 25.
    Ho W-H et al (2009) Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Syst Appl 36(2):3216–3222MathSciNetCrossRefGoogle Scholar
  26. 26.
    Lo S-P (2003) An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. J Mater Process Technol 142(3):665–675CrossRefGoogle Scholar
  27. 27.
    Ho S-Y et al (2002) 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(13):1441–1446CrossRefGoogle Scholar
  28. 28.
    Jiao Y et al (2004) Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations. Int J Mach Tools Manuf 44(15):1643–1651CrossRefGoogle Scholar
  29. 29.
    Kirby ED, Chen JC, Zhang JZ (2006) Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations. Expert Syst Appl 30(4):592–604CrossRefGoogle Scholar
  30. 30.
    Zhong Z, Khoo L, Han S (2006) Prediction of surface roughness of turned surfaces using neural networks. Int J Adv Manuf Technol 28(7-8):688–693CrossRefGoogle Scholar
  31. 31.
    Wang X, Feng C (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356CrossRefGoogle Scholar
  32. 32.
    Reddy NSK, Rao PV (2005) Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling. Int J Adv Manuf Technol 26(11-12):1202–1210CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • AmirMahyar Khorasani
    • 1
  • Mohammad Reza Soleymani Yazdi
    • 2
  1. 1.School of EngineeringDeakin UniversityWaurn PondsAustralia
  2. 2.Mechanical Engineering DepartmentIH UniversityTehranIran

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