Primetals Technologies Austria GmbH (Linz, Austria):

Bergmann M., Mag. Eng., Dept. of Technology and Innovation, Hot and Cold Rolling Mills, E-mail: martinbergmann@primetals.com
Krimpelstaetter K., Mag. Eng., Dr., Dept. of Technology and Innovation, Hot and Cold Rolling Mills

Quaker Chemical B.V. (Uithoorn, Netherlands):
Smeilders B., Dr., Scientific Researcher
Schellingerhout P., Manager on Scientific-Research and Engineering Works

voestalpine Stahl GmbH (Linz, Austria):
Paesold D., Mag. Eng., Dr., Senior Expert on Research and Development of Coated Strip

Johannes Kepler University (Linz, Austria):
Strasser D., Mag. Eng., Research Assistant
Kainz A., Mag. Eng., Dr., University Assistant
Zeman K., Mag. Eng., Prof., Dr., Bpard of the Institute on Development of Mechatronic Products

The works for research and testing of the previously developed tribological model of cold rolling have been continued. They were aimed on confirmation of applicability of this model in diff erent types of rolling mills and with various lubrication concepts. Typical usage of the model for periodical tandem rolling mill was displayed and good accuracy of rolling force prediction in combination with surface roughness, preliminary oil processing, lubricant composition was achieved. Researches were also conducted at continuous tandem rolling mill in order to determine the layer thickness deposited on metal surface. It is planned to continue these investigations for other rolling mills (i.e. reversing mill). Especial attention is paid now in the model development on possibility to take into account the effects of wear and tearing based on the roll gap tribology. The module hierarchic model concept can be the base for several elementary and specialized models for local parameters, e.g. roll wear and its effect on surface roughness and texture. Exact information about the conditions on the “strip – work roll” separation boundary (e.g. pressure, temperature, relative rate, roughness) is the necessary prerequisite for reliable prediction of wear effects. In this connection, the existing model suggests very promising approach for getting more deep knowledge about the process.

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