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

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Abstract

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.

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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.

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References

  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):1645–1658

    Article  Google Scholar 

  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):1303–1311

    Article  Google Scholar 

  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):1195–1208

    Article  Google Scholar 

  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):1185–1194

    Article  Google Scholar 

  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):1017–1026

    Article  Google Scholar 

  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):1573–1583

    Article  Google Scholar 

  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):1893–1903

    Article  Google Scholar 

  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):457–464

    Article  Google Scholar 

  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):735–744

    Article  Google Scholar 

  10. Liu T-I, Jolley B (2015) Tool condition monitoring (TCM) using neural networks. Int J Adv Manuf Technol 78(9-12):1999–2007

    Article  Google Scholar 

  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):1965–1978

    Article  Google Scholar 

  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):331–339

    Article  Google Scholar 

  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):343–354

    Article  Google Scholar 

  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):203–214

    Article  Google Scholar 

  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):1755–1768

    Article  Google Scholar 

  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):262–269

    Article  Google Scholar 

  17. Huang BP, Chen JC, Li Y (2008) Artificial-neural-networks-based surface roughness Pokayoke system for end-milling operations. Neurocomputing 71(4):544–549

    Article  Google Scholar 

  18. Topal ES (2009) The role of stepover ratio in prediction of surface roughness in flat end milling. Int J Mech Sci 51(11):782–789

    Article  Google Scholar 

  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):1375–1385

    Article  Google Scholar 

  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):467–479

    Article  Google Scholar 

  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):167–179

    Article  Google Scholar 

  22. Brezocnik M, Kovacic M, Ficko M (2004) Prediction of surface roughness with genetic programming. J Mater Process Technol 157:28–36

    Article  Google Scholar 

  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):451–462

    Article  Google Scholar 

  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):11–16

    Article  Google Scholar 

  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):3216–3222

    Article  MathSciNet  Google Scholar 

  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):665–675

    Article  Google Scholar 

  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):1441–1446

    Article  Google Scholar 

  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):1643–1651

    Article  Google Scholar 

  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):592–604

    Article  Google Scholar 

  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):688–693

    Article  Google Scholar 

  31. Wang X, Feng C (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356

    Article  Google Scholar 

  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):1202–1210

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Engineering, Deakin University, Waurn Ponds, VIC, Australia, 3220

    AmirMahyar Khorasani

  2. Mechanical Engineering Department, IH University, Tehran, Iran

    Mohammad Reza Soleymani Yazdi

Authors
  1. AmirMahyar Khorasani
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  2. Mohammad Reza Soleymani Yazdi
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Correspondence to AmirMahyar Khorasani.

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Khorasani, A., Yazdi, M.R.S. Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. Int J Adv Manuf Technol 93, 141–151 (2017). https://doi.org/10.1007/s00170-015-7922-4

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  • DOI: https://doi.org/10.1007/s00170-015-7922-4

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