Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

RETRACTED ARTICLE: Optimization design and reality of the virtual cutting process for the boring bar based on PSO-BP neural networks

  • Neural Computing in Next Generation Virtual Reality Technology
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

This article wasretracted on 16 May 2024

This article has beenupdated

Abstract

Based on the traditional boring bar, a boring bar with friction damper is proposed in the paper. Firstly, the frequency response under different pressures is computed primarily based on the theory, which shows that the proposed boring bar has a certain vibration reduction effect. Secondly, the finite element model of the boring bar is built, and the first 6-order modes are computed, whose results are compared with the experimental value. As a result, the virtual reality of the boring bar is achieved. They are consistent with each other, which show that the finite element model is reliable. Then, the experimental cutting process of the boring bar is researched, which is compared with the simulation model with good coincidence. It is found from the result that the cutting simulation model of the boring bar is effective. Later, based on the verified simulation model, the positive pressure between the friction vibrator and boring bar, cutting speed, feed rate, back cutting depth and other parameters are changed to study the vibration reduction effects of the boring bar with friction damper. PSO (particle swarm optimization)-BP (backpropagation) neural network is then used to optimize the cutting process of the boring bar, and the optimal cutting parameters can be obtained. Finally, these optimized parameters are applied in the boring bar, the vibration reduction effect of the boring bar is verified by means of experiments, and the corresponding result shows that the proposed optimization in this paper is feasible. We can obtain higher quality work piece when we use this boring bar in the actual engineering.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Change history

References

  1. Mei D, Kong T, Shih AJ et al (2009) Magnetorheological fluid-controlled boring bar for chatter suppression. J Mater Process Technol 209(4):1861–1870

    Article  Google Scholar 

  2. Moradi H, Bakhtiari-Nejad F, Movahhedy MR (2008) Tuneable vibration absorber design to suppress vibrations: an application in boring manufacturing process. J Sound Vib 318(1):93–108

    Article  Google Scholar 

  3. Miguelez MH, Rubio L, Loya JA et al (2010) Improvement of chatter stability in boring operations with passive vibration absorbers. Int J Mech Sci 52(10):1376–1384

    Article  Google Scholar 

  4. Sims ND (2007) Vibration absorbers for chatter suppression: a new analytical tuning methodology. J Sound Vib 301(3):592–607

    Article  Google Scholar 

  5. Yao Z, Chen Z, Mei D (2011) Chatter suppression by parametric excitation: model and experiments. J Sound Vib 330(13):2995–3005

    Article  Google Scholar 

  6. Andrén L, Håkansson L, Brandt A et al (2004) Identification of motion of cutting tool vibration in a continuous boring operation—correlation to structural properties. Mech Syst Signal Process 18(4):903–927

    Article  Google Scholar 

  7. Zhai P, Zhang CR, Liu SY, Wang HT (2006) Dynamic design of a high frequency response servo bar used for boring. Manuf Technol Mach Tool 8:103–106

    Google Scholar 

  8. Mei D, Yao Z, Kong T et al (2010) Parameter optimization of time-varying stiffness method for chatter suppression based on magnetorheological fluid-controlled boring bar. Int J Adv Manuf Technol 46(9–12):1071–1083

    Article  Google Scholar 

  9. Al-Wedyan HM, Bhat RB, Demirli K (2007) Whirling vibrations in boring trepanning association deep hole boring process: analytical and experimental investigations. J Manuf Sci Eng 129(1):48–62

    Article  Google Scholar 

  10. Yi J, Wang Q, Zhao D et al (2007) BP neural network prediction-based variable-period sampling approach for networked control systems. Appl Math Comput 185(2):976–988

    Google Scholar 

  11. Zhang JH, Xie AG, Shen FM (2007) Multi-objective optimization and analysis model of sintering process based on BP neural network. J Iron Steel Res Int 14(2):1–5

    Article  Google Scholar 

  12. Assarzadeh S, Ghoreishi M (2008) Neural-network-based modeling and optimization of the electro-discharge machining process. Int J Adv Manuf Technol 39(5–6):488–500

    Article  Google Scholar 

  13. Park JB, Lee KS, Shin JR et al (2005) A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Trans Power Syst 20(1):34–42

    Article  Google Scholar 

  14. Del Valle Y, Venayagamoorthy GK, Mohagheghi S et al (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evolut Comput 12(2):171–195

    Article  Google Scholar 

  15. Jiang M, Luo YP, Yang SY (2007) Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inf Process Lett 102(1):8–16

    Article MathSciNet  Google Scholar 

  16. Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33(3):859–871

    Article  Google Scholar 

  17. Khan K, Sahai A (2012) A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Int J Intell Syst Appl 4(7):23

    Google Scholar 

  18. Ping W, Huang Z, Zhang M et al (2008) Mechanical property prediction of strip model based on PSO-BP neural network. J Iron Steel Res Int 15(3):87–91

    Article  Google Scholar 

  19. Taormina R et al (2015) Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines. J Hydrol 529(3):1617–1632

    Article  Google Scholar 

  20. Zhang J et al (2009) Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. J Univers Comput Sci 15(4):840–858

    Google Scholar 

  21. Wang WC et al (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29(8):2655–2675

    Article MathSciNet  Google Scholar 

  22. Zhang SW et al (2009) Dimension reduction using semi-supervised locally linear embedding for plant leaf classification. Lect Notes Comput Sci 5754:948–955

    Article  Google Scholar 

  23. Wu CL et al (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1–4):80–93

    Article  Google Scholar 

  24. Chau KW et al (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinform 12(4):458–473

    Article  Google Scholar 

  25. Illias HA, Chai XR, Mokhlis H (2015) Transformer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques. PLoS ONE 10(6):e0129363

    Article  Google Scholar 

  26. Lv Z, Tek A, Da Silva F et al (2013) Game on, science-how video game technology may help biologists tackle visualization challenges. PLoS ONE 8(3):e57990

    Article  Google Scholar 

  27. Wei W, Xu Q, Wang L et al (2014) GI/Geom/1 queue based on communication model for mesh networks. Int J Commun Syst 27(11):3013–3029

    Article  Google Scholar 

  28. Lv Z, Chen G, Zhong C et al (2012) A framework for multi-dimensional webgis based interactive online virtual community. Adv Sci Lett 7(1):215–219

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, 150001, China

    Yu-shan Sun

  2. Technology Research and Development Center, Harbin No. 1 Tool Manufacture Corporation Ltd., Harbin, 150078, China

    Qiang Zhang

Authors
  1. Yu-shan Sun

    You can also search for this author inPubMed Google Scholar

  2. Qiang Zhang

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toYu-shan Sun.

Ethics declarations

Conflict of interest

We declared that this manuscript has no any conflict of interest and was not submitted and published in the other journal, and it was only submitted to Neural Computing and Applications.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Ys., Zhang, Q. RETRACTED ARTICLE: Optimization design and reality of the virtual cutting process for the boring bar based on PSO-BP neural networks.Neural Comput & Applic29, 1357–1367 (2018). https://doi.org/10.1007/s00521-017-2904-0

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp