Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-27T00:39:07.742Z Has data issue: false hasContentIssue false

Control of Directional Properties in Aluminum Can Stock

Published online by Cambridge University Press:  29 November 2013

Get access

Extract

Aluminum alloy 3004 (Al-1.0 Mn-1.0 Mg) is the predominant material of choice for use in rigid beverage containers. The mechanical property requirements of the draw and iron forming operation dictate careful control of the aluminum sheet production parameters. The demand for ever lighter weight beverage containers has also continued to place increased demands on the strength requirements of the aluminum can stock. To achieve the strength requirements, the sheet must be produced in a highly cold-worked state. A practical consequence of this required level of cold work is pronounced mechanical and crystallographic anisotropy. Without careful control of can stock production parameters, pronounced rolling textures can lead to an undesirable directionality of properties that produce ears, formed during the deep drawing operation of can making, located 45° to the rolling direction. Figure 1 shows the rolling texture ears in a drawn and ironed beverage can.

This article illustrates the minimization and control of rolling texture earing in cold rolled 3004 alloy beverage can stock through modification of the fabricating process, application of metallurgical theory, optimization of hot rolling practice through empirical experimentation, and process control practices.

Type
Materials Manufacturing
Copyright
Copyright © Materials Research Society 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Sanders, R.E. Jr., Lege, D.J., and Hartman, T.L., Proc. 8th Int. Light Metals Conf. (Leoben, Vienna, 1987) p. 226334.Google Scholar
2.Hutchinson, W.B., Oscarsson, A., and Karlsson, A., Mater. Sci. and Technol., 5 (1989), p. 11181127.CrossRefGoogle Scholar
3.Oscarsson, A., Hutchinson, W.B., and Karlsson, A., Proc. 8th Int. Light Metals Conf. (Leoben, Vienna, 1987), p. 531534.Google Scholar
4.Humphreys, F.J., Proc. 1st Riso Int. Symp. (1980) p. 3544.Google Scholar
5.Hutchinson, W.B. and Nes, E., Proc. 7th Riso Int. Symp. (1986) p. 107122.Google Scholar
6.Humphreys, F.J. and Jensen, D. Juul, Proc. 7th Riso Int. Symp. (1986) p. 93106.Google Scholar
7.Box, G.E.P., Hunter, W.G., and Hunter, J.S., in Statistics for Experimenters (Wiley & Sons, 1978), p. 291–434, 585603.Google Scholar
8.Draper, N. and Smith, H., in Applied Regression Analysis, 2nd ed. (Wiley & Sons, 1981), p. 412422.Google Scholar
9.Automation of Tandem Mills, edited by Bryant, G.F. (The Iron and Steel Institute, 1973).Google Scholar
10.Western Electric, Statistical Quality Control Handbook, 2nd ed. (Delmar Printing Co., 1983).Google Scholar
11.Maragah, H.D. and Woodall, W.H., Proc. Joint Statistical Meetings (American Statistical Society, 1988).Google Scholar
12.Shewhart, W.A., in Economic Control of Quality and Manufactured Products (Van Nostrand Reinhold, 1931).Google Scholar
13.Box, G.E.P. and Jenkins, G.M., in Time Series Analysis, Forescasting, and Control (Holden Day, 1970).Google Scholar