NDLI: Predicting Post-rolling Flatness by Statistical Analysis

Content Provider	IEEE Xplore Digital Library
Author	Uppgard, T.
Copyright Year	2007
Description	Author affiliation: Orebro Univ., Vasteras (Uppgard, T.)
Abstract	A concept to improve post-rolling flatness and offer flat products to the end customer would decrease substantially run-around scrap. This would mean lower energy consumption and lower environmental load per rolled strip. Part of the concept is advanced prediction tools. This paper reports current work in post-rolling flatness prediction of cold-rolled metal strip. The work was tested in an aluminium mill in Sweden where 8-series aluminium is produced. On-line measurements are made in a cold rolling mill and post-rolling measurements in a tension levelling line, using the same measurement technique in both processing lines. This allows measurements to be easily compared. There are too many thermal and mechanical parameters to make a reliable analytical model of the post-rolling flatness. Instead, two statistical methods to predict the post-rolling flatness are evaluated: multiple linear regression and artificial neural networks. Results show that both techniques are suitable for the purpose, but multiple linear regression is preferable.
Starting Page	2764
Ending Page	2769
File Size	267543
Page Count	6
File Format	PDF
ISBN	9781424407361
DOI	10.1109/ICIEA.2007.4318915
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistical analysis Strips Aluminum Milling machines Linear regression Energy consumption Testing Measurement techniques Analytical models Artificial neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Predicting patients survival using supervised techniques

Flatness prediction model based on wavelet transform

Predicting atmospheric ozone using neural networks as compared to some statistical methods

Neural control of a steel rolling mill

Fuzzy neural networks control for hydraulic AGC system of aluminum cold rolling mill

Control for asymmetric flatness deviation of cold-rolled strip

Neural networks and fuzzy rules based control for cold rolling process via sensitivity factors

Mathematical models and flatness control of an aluminum foil rolling mill

Neural network application in aluminium hot-roll process

Predicting Post-rolling Flatness by Statistical Analysis

Similar Documents

Predicting patients survival using supervised techniques

Flatness prediction model based on wavelet transform

Predicting atmospheric ozone using neural networks as compared to some statistical methods

Neural control of a steel rolling mill

Fuzzy neural networks control for hydraulic AGC system of aluminum cold rolling mill

Control for asymmetric flatness deviation of cold-rolled strip

Neural networks and fuzzy rules based control for cold rolling process via sensitivity factors

Mathematical models and flatness control of an aluminum foil rolling mill

Neural network application in aluminium hot-roll process

Predicting Post-rolling Flatness by Statistical Analysis