NDLI: Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection

Content Provider	Springer Nature Link
Author	Agarwal, Kuldeep Shivpuri, Rajiv
Copyright Year	2013
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.
Starting Page	1289
Ending Page	1299
Page Count	11
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	25
Issue Number	6
e-ISSN	15728145
Language	English
Publisher	Springer US
Publisher Date	2013-01-24
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hot bar rolling Automated inspection Bayesian hierarchical modeling Markov Chain Monte Carlo algorithm (MCMC) Production/Logistics/Supply Chain Manufacturing, Machines, Tools Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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