Abstract
There are various surface defects which occur during the hot rolling of steels. It is difficult to correctly identify and control these defects due to the different inspection techniques on different materials and sizes. Also, the statistical data analysis techniques typically used like the principal component analysis, factor analysis etc. require a lot of plant data and are computationally very intensive. Before a detailed analysis of the actual cause of the defects can be done, it is necessary to separate the defects as those coming from the continuous casting or the rolling mill. Once this is done, analysis on the individual components can then be completed to find the root cause. To accomplish both these analysis, Bayesian hierarchical modeling is done on the automated inspection of the bars to form a causal relationship of the defects to the process equipments. Variance reduction model is used at the top of the analysis and regression models are used in the next level.
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Acknowledgments
The authors would like to thank all the people associated with the data collection. The authors would also like to thank Dr. Prem Goel (Dept. of Statistics, OSU) for his insight into the Bayesian modeling. The research was carried out as a part of NSF STTR project RIE (IIP-0646502).
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Agarwal, K., Shivpuri, R. Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection. J Intell Manuf 25, 1289–1299 (2014). https://doi.org/10.1007/s10845-013-0730-5
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DOI: https://doi.org/10.1007/s10845-013-0730-5