Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA)

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

An artificial neural network (ANN) optimized by genetic algorithm (GA) is an established prediction model of bending force in hot strip rolling. The data are collected from factory of steel manufacture. Entrance temperature and thickness, exit thickness, strip width, rolling force, rolling speed, roll shifting, target profile, and yield strength of strip are selected to be independent variables as network inputs. MATLAB software is utilized for establishing GA-ANN model and achieving the purpose of obtaining the bending force as results of setup model, as well as the GA method is used to optimize the initial weights and biases of the backpropagation neural network. Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), and correlation coefficient are adapted to evaluate the performance of the model. The predictive results are compared with the measured results to verify the accuracy of the GA-ANN prediction model. It is found that the optimization effect is the best with the population size 40 crossover probability of 0.7 and the mutation probability of 0.05 at the same time, the fitness function value can reach 80.7. In addition, the ANN architecture 9-11-1 trained with Bayesian regulation “trainbr” function has the best performance with mean absolute error of 0.01 and correlation coefficient of 0.983. With a deeper understanding of neural networks through the analysis of the GA-ANN model, the proposed model can be flexibly used for on-line controlling and rolling schedule optimizing.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Jeon E, Kim S (2000) A study on the texturing of work roll for temper rolling. J Korean Soc Mach Tool Eng 9(4):7–16

    Google Scholar 

  2. Portmann NF, Lindhoff D, Sorgel G, Gramckow O (1995) Application of neural networks in rolling mill automation. Iron Steel Eng 72(2):33–36

    Google Scholar 

  3. Larkiola J, Myllykoski P, Korhonen A, Cser L (1998) The role of neural networks in the optimisation of rolling processes. J Mater Process Technol 80:16–23

    Article  Google Scholar 

  4. Yao X (1996) Applications of artificial intelligence for quality control at hot strip mills

  5. Pican N, Alexandre F, Bresson P (1996) Artificial neural networks for the presetting of a steel temper mill. IEEE Expert 11(1):22–27

    Article  Google Scholar 

  6. Lee D, Lee Y (2002) Application of neural-network for improving accuracy of roll-force model in hot-rolling mill. Control Eng Pract 10(4):473–478

    Article  Google Scholar 

  7. Poppe T, Obradovic D, Schlang M (1995) Neural networks-reducing energy and raw-materials requirements. Siemens Rev:24–27

  8. Chun M, Biglou J, Lenard J, Kim J (1999) Using neural networks to predict parameters in the hot working of aluminum alloys. J Mater Process Technol 86(1):245–251

    Article  Google Scholar 

  9. Lee D, Choi S (2004) Application of on-line adaptable neural network for the rolling force set-up of a plate mill. Eng Appl Artif Intell 17(5):557–565

    Article  Google Scholar 

  10. Son J, Lee D, Kim I, Choi S (2005) A study on on-line learning neural network for prediction for rolling force in hot-rolling mill. J Mater Process Technol 164:1612–1617

    Article  Google Scholar 

  11. Moussaoui A, Selaimia Y, Abbassi HA Hybrid hot strip rolling force prediction using a Bayesian trained artificial neural network and analytical models. In: American Journal of Applied Sciences, Citeseer

  12. Yang Y, Linkens D, Talamantes-Silva J, Howard I (2003) Roll force and torque prediction using neural network and finite element modelling. ISIJ Int 43(12):1957–1966

    Article  Google Scholar 

  13. Yang Y, Linkens D, Talamantes-Silva J (2004) Roll load prediction—data collection, analysis and neural network modelling. J Mater Process Technol 152(3):304–315

    Article  Google Scholar 

  14. Bagheripoor M, Bisadi H (2013) Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process. Appl Math Model 37(7):4593–4607

    Article  Google Scholar 

  15. Haykin S, Lippmann R (1994) Neural networks, a comprehensive foundation. Int J Neural Syst 5(4):363–364

    Article  MATH  Google Scholar 

  16. Bishop CM (2006) Pattern recognition, vol 128. Machine learning

  17. Deng J, Gu D, Li X, Yue Z (2005) Structural reliability analysis for implicit performance functions using artificial neural network. Struct Saf 27(1):25–48

    Article  Google Scholar 

  18. Khayet M, Cojocaru C (2012) Artificial neural network modeling and optimization of desalination by air gap membrane distillation. Sep Purif Technol 86:171–182

    Article  Google Scholar 

  19. Mukhopadhyay A, Iqbal A (2005) Prediction of mechanical properties of hot rolled, low-carbon steel strips using artificial neural network. Mater Manuf Process 20(5):793–812

    Article  Google Scholar 

  20. Mukhopadhyay A, Iqbal A (2006) Comparison of ANN and MARS in prediction of property of steel strips. Applied Soft Computing Technologies: The Challenge of Complexity: 329–341

  21. Shahani A, Setayeshi S, Nodamaie S, Asadi M, Rezaie S (2009) Prediction of influence parameters on the hot rolling process using finite element method and neural network. J Mater Process Technol 209(4):1920–1935

    Article  Google Scholar 

  22. Liu C, Ding W, Li Z, Yang C (2016) Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm. Int J Adv Manuf Technol 89(5):1–9

    Google Scholar 

  23. Holland John H (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan, Ann Arbor

    MATH  Google Scholar 

  24. Ma C, Zhao L, Mei X, Shi H, Yang J (2016) Thermal error compensation of high-speed spindle system based on a modified BP neural network. Int J Adv Manuf Technol 89(9):3071–3085

    Google Scholar 

  25. Yuce B, Rezgui Y, Mourshed M (2016) ANN–GA smart appliance scheduling for optimised energy management in the domestic sector. Energ Buildings 111:311–325

    Article  Google Scholar 

  26. Hou T, Su C, Liu W (2007) Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm. Powder Technol 173(3):153–162

    Article  Google Scholar 

  27. Khoualdia T, Hadjadj AE, Bouacha K, Abdeslam DO (2016) Multi-objective optimization of ANN fault diagnosis model for rotating machinery using grey rational analysis in Taguchi method. Int J Adv Manuf Technol 89(9):3009–3020

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhen-Hua Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, ZH., Gong, DY., Li, X. et al. Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA). Int J Adv Manuf Technol 93, 3325–3338 (2017). https://doi.org/10.1007/s00170-017-0711-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-0711-5

Keywords