Scale is highly detrimental to surface quality for tinplate products. As there are a large number of process variables at a typical hot mill, non-linear partial least squares was used to study the influence of these process parameters on tertiary scale formation. It was found that a logit model containing just two partial least squares components provided an adequate best fit to the process data with the most significant variables being the average temperatures at which the steel strip was entering the finishing and rolling mills, the average crop shear temperature and the percentage of phosphorus present within the strip.
HaapamakiJ, TamminenS, RoningJ: ‘Data mining methods in hot steel rolling for scale defect prediction’, Proc. AIA 2005, Innsbruck, Austria, February 2005, IASTED, 453–464.
2.
HaapamakiJ, RoningJ: ‘Genetic algorithms in hot steel rolling for scale defect prediction’,Eng. Technol., 2005, 5, 1–4.
3.
BoltH: ‘Understanding the properties of oxide scale on hot rolled strip’, Proc. METEC Congress 03/3rd Eur. Rolling Conf. (ERC), 16–18 June, Dusseldorf, Germany, June 2003.
4.
TaniguchiS, YamamotoK, MegumiD, ShibataT: ‘Characteristics of scale/substrate interface area of Si-containing low-carbon steels at high temperatures’,Mater. Sci. Eng. A, 2001, A308, 250–257.
5.
YangY, YangC, LinS, ChenC, TsaiW: ‘Effects of Si and its content on the scale formation on hot-rolled steel strips’,Mater. Chem. Phys., 2008, 112, 566–571.
6.
MuntherP, LenardJ: ‘The effect of scaling on interfacial friction in hot rolling of steels’,J. Mater. Process. Technol., 1999, 88, 105–113.
7.
H. Bolt: The Properties of Oxide Scales on Hot Rolled Steels: A Literature Review, Corus Research, Development & Technology, Ijmuiden Technology Centre, 2000.
8.
SunW, TieuAK, JiangZ, ZhuH, LuC: ‘Oxide scales growth of low-carbon steel at high temperatures’,J. Mater. Process. Technol., 2004, 155–156, 1300–1306.
9.
SilkN: ‘The practical aspects of hydraulic de-scaling’, Steel Times Int., Oct. 2001, 38–44.
10.
MarxBD: ‘Iteratively reweighted partial least squares estimation for generalized linear regression’,Technometrics, 1996, 38, (4), 374–381.
BastienP, TenenhausM: ‘PLS generalized linear regression: application to the analysis of life time data’,Comput. Stat. Data Anal., 2005, 48, (1), 17–46.
