Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

On line prediction of surface defects in hot bar rolling based on Bayesian hierarchical modeling

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Steel bar manufacturing process is a multistage process in which melted steel passes through multiple steps to form into a bar or rod of varying diameters. The most common cause of rejection of such products is the surface defects such as seams, scales, cracks etc. Often automated inspection in the form of image analysis is done to predict the formation of such defects. These inspections suffer from the fact that they do not include the process information and hence are not very reliable. This paper presents a novel methodology of prediction of surface defects in bar manufacturing. In this method, the surface images obtained by inspection methodology are decomposed into individual defects based on process knowledge. These are then decomposed into the physical quantities affecting them which are decomposed into design parameters on rolling mill. To achieve this decomposition, Bayesian hierarchical modeling is used. Data collected from sensors installed on rolling mill are used for model building and design. After the model is built, it is converted into an automated system called Diagnostics on Rolling Mill (DORM). DORM is installed on rolling mill and its performance is evaluated for several days. It is found that the model performs very closely with manual inspection and predicts surface defects with good accuracy. The Bayesian model also gives an insight into process parameters and physical quantities which affect the individual defects.

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
$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.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Agarwal, K., & Shivpuri, R. (2013). Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0730-5.

  • Agarwal, K., Shivpuri, R., Zhu, Y., Chang, T. S., & Huang, H. (2011). Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling. Expert Systems with Applications, 38(6), 7251–7262.

    Article  Google Scholar 

  • Altan, T., Kobayashi, S., & Kim, N. S. (1991). Three dimensional analysis and computer simulation of shape rolling by the finite and slab element method. International Journal of Machine Tool and Manufacture, 31, 553–563.

    Article  Google Scholar 

  • Berliner, L. M., Milliff, R. F., & Wikle, C. K. (2003). Bayesian hierarchical modeling of air-sea interaction. Journal of Geophysical Research. doi:10.1029/2002JC001413.

  • Beynon, J. H., & Krzyzanowski, M. (2002). Measurement of oxide properties for numerical evaluation of their failure under hot rolling conditions. Journal of Materials Processing Technology, 125–126, 398–404.

    Google Scholar 

  • Brimacombe, J. K. (2003). The challenge of quality in continuous casting processes. Metallurgical and Materials Transactions A, 30A, 1899–1912.

    Google Scholar 

  • Chen, H.-Y., & Huang, S.-J. (2011). Self-organizing fuzzy controller for the molten steel level control of a twin-roll strip casting process. Journal of Intelligent Manufacturing, 22(4), 619–626.

    Article  Google Scholar 

  • Cressie, N., Buxton, B. E., Calder, C. A., Craigmile, P. F., Dong, C., McMillan, N. J., et al. (2007). From sources to biomarkers: A hierarchical Bayesian approach for human exposure modeling. Journal of Statistical Planning and Inference, 137, 3361–3375.

    Article  Google Scholar 

  • Gelfand, A. E., & Smith, A. F. M. (1990). Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398–409.

    Article  Google Scholar 

  • Ginzburg, V. (2005). Metallurgical design of flat rolled steels. Boca Raton, FL: CRC press.

    Google Scholar 

  • Huang, H., Gutches, D., & Chang, T.S. (2004). Imaging based in-line surface defect inspection for bar rolling. In Proceedings of the AIST iron and steel conference and exposition, Nashville, TN, USA (pp. 1–12).

  • Ji, M., & Shivpuri, R. (2006). Reduction of random seams in hot rolling through FEM based sensitivity analysis. Material Science and Engineering A, 425(1–2), 156–166.

    Article  Google Scholar 

  • Jin, N., Zhou, S., & Chang, T. S. (2004). Identification of impacting factors of surface defects in hot rolling processes using multi-level regression analysis. Transactions of NAMRI/SME, 32, 557–564.

    Google Scholar 

  • Kiuchi, M., & Yanagimoto, J. (1991). Advanced computer aided simulation technique for 3D rolling process. Advanced Technology of Plasticity, 2, 639–644.

    Google Scholar 

  • Kuhn, H. A. (2007). Workability theory and application in bulk forming processes. Handbook of metal forming (Vol. 14A). Geauga County, OH: ASM International.

  • Kwon, H. C., Lee, H. W., Kim, H. Y., Im, Y. I., Park, H. D., & Lee, D. E. (2009). Surface wrinkle defect of carbon steel in the hot bar rolling process. Journal of Materials Processing Technology, 209(9), 4476–4483.

    Article  Google Scholar 

  • Lee, D. Y., Cho, H. S., & Cho, D. Y. (2000). A neural network model to determine the plate width set-up value in a hot plate mill. Journal of Intelligent Manufacturing, 11(6), 547–557.

    Article  Google Scholar 

  • Li, H., Lian, X., Guo, C., & Zhao, P. (2013). Investigation on early fault classification for rolling element bearing based on the optimal frequency band determination. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0772-8.

  • Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10, 325–337.

    Article  Google Scholar 

  • Mori, K. (1990). General purpose FEM simulator for 3-D rolling. Advanced Technology of Plasticity, 4, 1773–1778.

    Google Scholar 

  • Paralikas, J., Salonitis, K., & Chryssolouris, G. (2009). Optimization of the roll forming process parameters—a semi empirical approach. International Journal of Advanced Manufacturing Technology, 47(9–12), 1041–1052.

    Google Scholar 

  • Paralikas, J., Salonitis, K., & Chryssolouris, G. (2011). Investigation of the effect of roll forming pass design on main redundant deformations on profiles from AHSS. The International Journal of Advanced Manufacturing Technology, 56(5–8), 475–491.

    Article  Google Scholar 

  • Park, J. J., & Oh, S. I. (1990). Application of 3D FEM analysis to shape rolling process. Transaction ASME Journal of Engineering, 112, 36–46.

    Article  Google Scholar 

  • Pauskar P., Shivpuri R., Cho H., & Kim N. (1997) An FEM based model of metal flow and microstructure evolution in continuous hot rolling. Transactions of the NAMRI/SME, XXV, 67–74.

  • R Development Core Team. (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN:3-900051-07-0. http://www.R-project.org.

  • Shi, J., & Li, J. (2007). Knowledge discovery from observational data for process control through causal Bayesian networks. IIE Transactions, 39, 681–690.

    Article  Google Scholar 

  • Wang, K. (2007). Applying data mining to manufacturing: the nature and implications. Journal of Intelligent Manufacturing, 18(4), 487–495.

    Article  Google Scholar 

  • Zhang, X., Xu, T., Zhao, L., Fan, H., & Zang, J. (2013). Research on flatness intelligent control via GA-PIDNN. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0789-z.

Download references

Acknowledgments

The authors would like to thank all the personnel involved in the data collection and implementation at Steel Plant especially O.G. Technologies. Part of this work was funded by a grant from the STTR program of the National Science Foundation (IIP-0646502). The Bayesian formulation was supported under grant from NSF CMMI Engineering Design and Innovation Group. (Grant #1000330).

Author information

Authors and Affiliations

  1. Department of Automotive and Manufacturing Engineering Technology, Minnesota State University, Mankato, 205 Trafton Science Center E, Mankato, MN, 56001, USA

    Kuldeep Agarwal

  2. Department of Integrated Systems Engineering, The Ohio State University, 210 Baker Systems Bldg, 1971 Neil Avenue, Columbus, OH, 43210, USA

    Rajiv Shivpuri

Authors
  1. Kuldeep Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajiv Shivpuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kuldeep Agarwal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Agarwal, K., Shivpuri, R. On line prediction of surface defects in hot bar rolling based on Bayesian hierarchical modeling. J Intell Manuf 26, 785–800 (2015). https://doi.org/10.1007/s10845-013-0834-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-013-0834-y

Keywords

Search

Navigation