Top > IJAT > Predicting Surface Roughness of Dry Cut Grey Cast Iron Based o ...

single-au.php

IJAT Vol.14 No.2 pp. 217-228
doi: 10.20965/ijat.2020.p0217
(2020)

Paper:

Views over last 60 days: 843

Predicting Surface Roughness of Dry Cut Grey Cast Iron Based on Cutting Parameters and Vibration Signals from Different Sensor Positions in CNC Turning

Jonny Herwan†, Seisuke Kano, Oleg Ryabov, Hiroyuki Sawada, Nagayoshi Kasashima, and Takashi Misaka

National Institute of Advanced Industrial Science and Technology (AIST)
1-2-1 Namiki, Tsukuba, Ibaraki 305-8564, Japan

†Corresponding author

Received:
January 17, 2019
Accepted:
October 18, 2019
Published:
March 5, 2020
Creative Commons License
Keywords:
artificial neural network, grey cast iron, multiple regression model, surface roughness, vibration
Abstract

During the turning process, cast iron is directly shattered to become particles. This mechanism means the surface roughness cannot be predicted using the kinematic equation. This paper provides surface roughness predictions using two methods, the multiple regression model (MRM) and artificial neural network (ANN). Cutting parameters and vibration signals are considered input variables in both methods. This work also overcomes the common sensor position limitation (tool shank) and provides a safe and efficient solution. The prediction values from MRM and ANN show accurate results compared to the measured surface roughness, with the average error of less than 8%. Furthermore, the proposed sensor position, at the turret bed, also exhibits similar prediction accuracy to a sensor at the tool shank, hence promising feasible industrial application.

Cite this article as:
J. Herwan, S. Kano, O. Ryabov, H. Sawada, N. Kasashima, and T. Misaka, “Predicting Surface Roughness of Dry Cut Grey Cast Iron Based on Cutting Parameters and Vibration Signals from Different Sensor Positions in CNC Turning,” Int. J. Automation Technol., Vol.14 No.2, pp. 217-228, 2020.
Data files:
References
  1. [1] Modern Casting Staff, “Census of World Casting Production,” Modern Casting, p. 24, December 2018.
  2. [2] M. P. Groover, “Fundamentals of modern manufacturing: materials, processes, and systems (3rd Ed.),” John Wiley & Sons, 2007.
  3. [3] R. C. Voigt, P. H. Marwanga, and P. H. Cohen, “Machinability of Gray Iron – Mechanics of Chip Formation,” Int. J. Cast Metals Research, Vol.11, pp. 567-572, 1999.
  4. [4] K. D. Thoben, S. Wiener, and T. Wuest, “Industrie 4.0 and smart manufacturing – A review of research issues and application examples,” Int. J. Automation Technol., Vol.11, No.1, pp. 4-16, 2017.
  5. [5] K. Liu, P. Zhong, Q. Zeng, D. Li, and S. Li, “Application modes of cloud manufacturing and program analysis,” J. of Mechanical Science and Technology, Vol.31, pp. 157-164, 2017.
  6. [6] J. Herwan, S. Kano, R. Oleg et al., “Cyber-physical system architecture for machining production line,” Proc. of 2018 IEEE Industrial Cyber-Physical Systems (ICPS), Saint Petersburg, Russia, pp. 387-391, doi: 10.1109/ICPHYS.2018.8387689, 2018.
  7. [7] R. Teti, K. Jemielniak, G. O’Donnell, and D. Dornfeld, “Advanced monitoring of machining operations,” CIRP Annals – Manufacturing Technology, Vol.59, pp. 717-732, 2010.
  8. [8] G. Y. Lee et al., “Machine health management in smart factory: A review,” J. of Mechanical Science and Technology, Vol.32, pp. 987-1009, 2018.
  9. [9] A. Caggiano, T. Segreto, and R. Teti, “Cloud manufacturing framework for smart manufacturing of machining,” Procedia CIRP, Vol.55, pp. 248-253, 2016.
  10. [10] M. Munawar, J. C. Chen, and N. A. Mufti, “Investigating of Cutting Parameters Effect for Minimization of Surface Roughness in Internal Turning,” Int. J. Precis. Eng. Manuf., Vol.12, pp. 121-127, 2011.
  11. [11] L. Bouzid, M. A. Yallese, K. Chaoui et al., “Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology,” Proc. IMechE Part B: J. Engineering Manufacture, Vol.229, pp. 45-61, 2014.
  12. [12] B. Bhardwaj, R. Kumar, and P. K. Singh, “Surface roughness (Ra) prediction model for turning of AISI 1019 steel using response surface methodology and Box-Cox transformation,” Proc. IMechE Part B: J. Engineering Manufacture, Vol.228, pp. 223-232, 2014.
  13. [13] E. D. Kirby, Z. Zhang, and J. C. Chen, “Development of an Accelerometer-based Surface Roughness Prediction System in Turning Operations using Multiple Regression Techniques,” J. Ind. Technol., Vol.20, pp. 1-8, 2004.
  14. [14] E. A. Al Bahkali, A. E. Ragab, E. A. El-Danaf et al., “An Investigation of Optimum Cutting Conditions in Turning Nodular Cast Iron using Carbide Inserts with Different Nose Radius,” Proc. IMechE Part B: J. Engineering Manufacture, Vol.230, pp. 1584-1591, 2016.
  15. [15] Z. Hessainia, A. Belbah, M. A. Yallese et al., “On the Prediction of Surface Roughness in the Hard Turning based on Cutting Parameters and Tool Vibrations,” Measurement, Vol.46, pp. 1671-1681, 2013.
  16. [16] V. Upadhyay, P. K. Jain, and N. K. Mehta, “In-process Prediction of Surface Roughness in Turning of Ti-6Al-4V Alloy using Cutting Parameters and Vibration Signals,” Measurement, Vol.46, pp. 154-160, 2013.
  17. [17] T. Ozel and Y. Karpat, “Predictive Modeling of Surface Roughness and Tool Wear in Hard Turning using Regression and Neural Networks,” Int. J. of Machine Tools & Manufacture, Vol.45, pp. 467-479, 2005.
  18. [18] R. Azouzi and M. Guillot, “On-line Prediction of Surface Finish and Dimensional Deviation in Turning using Neural Network based Sensor Fusion,” Int. J. of Machine Tools and Manufacture, Vol.37, pp. 1201-1217, 1997.
  19. [19] K. A. Risbood, U. S. Dixit, and A. D. Sahasrabudhe, “Prediction of Surface Roughness and Dimensional Deviation by Measuring Cutting Forces and Vibrations in Turning Process,” J. of Materials Processing Technology, Vol.132, pp. 203-214, 2003.
  20. [20] Y. Chen, R. Sun, Y. Gao et al., “A nested-ANN Prediction Model for Surface Roughness Considering the Effects of Cutting Forces and Tool Vibrations,” Measurement, Vol.98, pp. 25-34, 2017.
  21. [21] M. Mia and N. R Dhar, “Prediction of Surface Roughness in Hard Turning under High Pressure Coolant using Artificial Neural Network,” Measurement, Vol.92, pp. 464-474, 2016.
  22. [22] M. Mia, M. S. Morshed, M. Kharshiduzzaman et al., “Prediction and Optimization of Surface Roughness in Minimum Quantity Coolant Lubrication Applied Turning of High Hardness Steel,” Measurement, Vol.118, pp. 43-51, 2018.
  23. [23] E. D. Kirby and J. C. Chen, “Development of a Fuzzy-nets-based Surface Roughness Prediction System in Turning Operations,” Comput. Ind. Eng., Vol.53, pp. 30-42, 2007.
  24. [24] Y. Jiao, S. Lei, Z. J. Pei et al., “Fuzzy Adaptive Networks in Machining Process Modeling: Surface Roughness Prediction for Turning Operations,” Int. J. of Machine Tools and Manufacture, Vol.44, pp. 1643-1651, 2004.
  25. [25] R. Panneer, S. P. Harisubramanyabalaji, C. A. Sribalaji et al., “Prediction of Surface Roughness using Spectral Analysis and Image Comparison of Audio Signals,” Int. J. Precis. Eng. Manuf., Vol.17, pp. 709-715, 2016.
  26. [26] D. R. Salgado, I. Cambero, A. Marcelo et al., “Surface roughness prediction based on the correlation between surface roughness and cutting vibrations in dry turning with TiN-coated carbide tools,” Proc. IMechE Part B: J. Engineering Manufacture, Vol.223, pp. 1193-1205, 2009.
  27. [27] E. G. Plaza and P. J. N. López, “Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations,” Mechanical Systems and Signal Processing, Vol.98, pp. 902-919, 2018.
  28. [28] J. Herwan, S. Kano, R. Oleg et al., “Comparing Vibration Sensor Positions in CNC Turning for a Feasible Application in Smart Manufacturing System,” Int. J. Automation Technol., Vol.12, No.3, pp. 282-289, 2018.
  29. [29] https://www.bksv.com/en/products/transducers/vibration/Vibration-transducers/accelerometers/4535-B-001      [Accessed December 1, 2017]
  30. [30] http://www.hurco.com/en-us/about-hurco/overview/pages/default.aspx [Accessed October 2, 2017]
  31. [31] http://www.mitsubishicarbide.com/mmus/en/product/catalog/index.html [Accessed February 19, 2018]
  32. [32] P. Angappan, S. Thangiah, and S. Subbarayan, “Taguchi-based grey relational analysis for modeling and optimizing machining parameters through dry turning of Incoloy 800H,” J. of Mechanical Science and Technology, Vol.31, pp. 4159-4165, 2017.
  33. [33] https://www.minitab.com/en-us/ [accessed July.16,2019].
  34. [34] https://blog.minitab.com/blog/adventures-in-statistics-2/regression-smackdown-stepwise-versus-best-subsets [Accessed July 16, 2019].
  35. [35] H. Demuth and M. Beale, “Neural Network Toolbox for use with MATLAB,” The MathWorks, Inc., 1997.
  36. [36] M. H. Beale, M. T. Hagan, and H. B. Demuth, “Neural network toolbox user’s guide,” The MathWorks, Inc., 2018.
  37. [37] A. Caggiano and L. Nele, “Artificial Neural Networks for Tool Wear Prediction Based on Sensor Fusion Monitoring of CFRP/CFRP Stack Drilling,” Int. J. Automation Technol., Vol.12, No.3, pp. 275-281, 2018.
  38. [38] G. Quintana, T. Rudolf, J. Ciurana, and C. Brecher, “Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks,” J. of Mechanical Science and Technology, Vol.25, pp. 2877-2886, 2011.
  39. [39] D. C. Montgomery, “Design and analysis of experiments (8th Ed.),” John Wiley & Sons, 2013.
  40. [40] https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/correlation-coefficient-formula/ [Accessed July 17, 2019].

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024