Skip to main content
SpringerLink
Log in

Artificial Neural Network Models for the Prediction of Metal Removal Rate in Rotary Ultrasonic Machining

  • Conference paper
  • First Online:
Advances in Intelligent Manufacturing

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

  • 436 Accesses

Abstract

The nickel-based superalloys are highly demanding material in modern industries, owing to its unique characteristics like elevated mechanical strength, toughness, and excellent elevated temperature stability. In the current study, the evaluation of the MRR for RUM is done by varying the different parameters like tool rotation; tool feed rate, tool profile, diamond abrasive size, and power rating. An attempt is processed to study the RUM parameters by modeling the process using artificial neural networks (ANN) and a feed-forward back-propagation neural network based on a response surface methodology, experimental design is developed to model the parameters. The developed model is found to be quite significant, influential, and flexible.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Briceno, J., El-Mounayri, H., & Mukhopadhyay, S. (2002). Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process. International Journal of Machine Tools and Manufacture, Retrieved from http://www.sciencedirect.com/science/article/pii/S0890695502000081

  2. Churi, N. J., Pei, Z. J., Shorter, D. C., & Treadwell, C. (2007). Rotary ultrasonic machining of silicon carbide: designed experiments. International Journal of Manufacturing Technology and Management, 12(1/2/3), 284–298. https://doi.org/10.1504/IJMTM.2007.014154

  3. Churi, N. J., Pei, Z. J., & Treadwell, C. (2006). Rotary ultrasonic machining of titanium alloy: Effects of machining variables. Machining Science and Technology. https://doi.org/10.1080/10910340600902124.

    Article  Google Scholar 

  4. Ciurana, J., Arias, G., & Ozel, T. (2009). Neural network modeling and Particle Swarm Optimization (PSO) of process parameters in pulsed laser micromachining of Hardened AISI H13 steel. Materials and Manufacturing Processes, 24(3), 358–368. https://doi.org/10.1080/10426910802679568.

    Article  Google Scholar 

  5. Daliakopoulos, I. N., Coulibaly, P., & Tsanis, I. K. (2005). Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(1–4), 229–240. https://doi.org/10.1016/j.jhydrol.2004.12.001.

    Article  Google Scholar 

  6. Dimla, D., Lister, P., & Leighton, N. (1997). Tool condition monitoring in metal cutting through application of MLP neural networks. Systems (Digest No: 1997/, Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=643166

  7. Dini, G. (1997). Literature database on applications of artificial intelligence methods in manufacturing engineering. CIRP Annals-College International de, Retrieved from https://scholar.google.co.in/scholar?hl=en&q=Literature+database+on+applications+of+artificial+intelligence+methods+in+manufacturing+engineering.&btnG=#0

  8. El-Sonbaty, I. A., Khashaba, U. A., Selmy, A. I., & Ali, A. I. (2008). Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach. Journal of Materials Processing Technology, 200(1–3), 271–278. https://doi.org/10.1016/j.jmatprotec.2007.09.006.

    Article  Google Scholar 

  9. Ezugwu, E. O., Fadare, D. A., Bonney, J., Da Silva, R. B., & Sales, W. F. (2005). Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. International Journal of Machine Tools and Manufacture, 45(12–13), 1375–1385. https://doi.org/10.1016/j.ijmachtools.2005.02.004.

    Article  Google Scholar 

  10. Feng, Q., Cong, W. L., Pei, Z. J., & Ren, C. Z. (2012). Rotary ultrasonic machining of carbon fiber-reinforced polymer: feasibility study. Machining Science and Technology, 16(3), 380–398. https://doi.org/10.1080/10910344.2012.698962.

    Article  Google Scholar 

  11. Hines, J. (1997). Fuzzy and neural approaches in engineering. New York: A Willey–interscience, Retrieved from http://project-2012-neuralnetwork.googlecode.com/svn/trunk/Papers/47421957-Fuzzy-Neural.pdf

  12. Hu, P., Zhang, J. M., Pei, Z. J., & Treadwell, C. (2002). Modeling of material removal rate in rotary ultrasonic machining: Designed experiments. Journal of materials processing technology, 129, 339–344.

    Article  Google Scholar 

  13. Kao, J., & Tarng, Y. (1997). A neutral-network approach for the on-line monitoring of the electrical discharge machining process. Journal of Materials Processing Technology, 69(1–3), 112–119. https://doi.org/10.1016/S0924-0136(97)00004-6.

    Article  Google Scholar 

  14. Kumar, J., & Khamba, J. S. J. (2008). An experimental study on ultrasonic machining of pure titanium using designed experiments. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 30(3). https://doi.org/10.1590/S1678-58782008000300008

  15. Montgomery, D. (2008). Design and Analysis of Experiments. Nature Biotechnology (Vol. 26). https://doi.org/10.1534/g3.113.008565

  16. Nabhan, T. M., & Zomaya, A. Y. (1994). Toward generating neural network structures for function approximation. Neural Networks, 7(1), 89–99. https://doi.org/10.1016/0893-6080(94)90058-2.

    Article  Google Scholar 

  17. Panda, D. K., & Bhoi, R. K. (2005). Artificial neural network prediction of material removal rate in electro discharge machining. Materials and Manufacturing Processes, 20(4), 645–672. https://doi.org/10.1081/AMP-200055033.

    Article  Google Scholar 

  18. Zou, X., Cong, W., Wu, N., Tian, Y., Pei, Z.J. and Wang, X. (2013). Cutting temperature in rotary ultrasonic machining of titanium : experimental study using novel Fabry-Perot fibre optic sensors. Journal of Manufacturing Research, 8(3), 250–261. https://doi.org/10.1504/IJMR.2013.055242

  19. Popli, D., & Gupta, M. (2017). Sequential procedure for selecting the ranges of process parameters in rotary ultrasonic machining. International Journal of Manufacturing Research, 12(3), 364–378.

    Article  Google Scholar 

  20. Popli, D., & Gupta, M. (2018). Investigation of machining rate and roughness for rotary ultrasonic drilling of Inconel 718 alloy with slotted diamond metal bonded tool. International Journal of Manufacturing Research, 13(1), 68–95.

    Article  Google Scholar 

  21. Popli, D., & Gupta, M. (2018). Investigation of the circularity and conicity of super alloy during rotary ultrasonic machining. Iranian Journal of Science and Technology, Transactions of Mechanical Engineering.. https://doi.org/10.1007/s40997-018-0197-2.

    Article  Google Scholar 

  22. Jadoun, R.S., Kumar, P. & Mishra, B.K. (2009). Taguchi’ s optimization of process parameters for production accuracy in ultrasonic drilling of engineering ceramics. Production Engineering 3(3), 243–253. https://doi.org/10.1007/s11740-009-0164-2

  23. Tsai, K.-M., & Wang, P.-J. (2001). Predictions on surface finish in electrical discharge machining based upon neural network models. International Journal of Machine Tools and Manufacture, 41(10), 1385–1403. https://doi.org/10.1016/S0890-6955(01)00028-1.

    Article  Google Scholar 

  24. Yegnanarayana, B. (1999). Artificial Neural Networks, 476

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Bharat Institute of Engineering & Technology, Hyderabad, Telengana, India

    Dipesh Popli

  2. School of Mechanical Engineering, Lovely Professional University, Phagwara, India

    Parveen Sharma

  3. Department of Mechanical Engineering, GD Goenka University, Gurugram, Haryana, India

    Shubham Verma

Authors
  1. Dipesh Popli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parveen Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shubham Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipesh Popli .

Editor information

Editors and Affiliations

  1. Mechanical Engineering, Opole University of Technology, Opole, Poland

    Grzegorz Krolczyk

  2. School of Mechanical Engineering, Lovely Professional University, Phagwara, India

    Chander Prakash

  3. Centre for Nanofibers & Nanotechnology, National University of Singapore, Singapore, Singapore

    Sunpreet Singh

  4. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    Joao Paulo Davim

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Popli, D., Sharma, P., Verma, S. (2020). Artificial Neural Network Models for the Prediction of Metal Removal Rate in Rotary Ultrasonic Machining. In: Krolczyk, G., Prakash, C., Singh, S., Davim, J. (eds) Advances in Intelligent Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4565-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4565-8_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4564-1

  • Online ISBN: 978-981-15-4565-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation