Laboratory simulation and modelling of the break-down rolling of AA3104.

Over the last few decades, the specifications for wrought aluminium products have
become increasingly strict. Not only are the dimensional tolerances important, but
also the material properties (strength, earing performance, corrosion resistance)
must meet specified levels. The material properties are controlled by the
microstructure. Hence the necessity for a modern aluminium plant to control the
microstructure of the finished product through the processing parameters.
This microstructure strongly depends on the microstructures produced during each
of the processing steps. Therefore, it is necessary to control the microstructure
throughout the production process. lt is thus imperative to know and model the
influence of the processing conditions at each step.
The present work focuses on one processing step: break-down rolling. During this
step, the thickness of the ingot is reduced from 500 to 25 mm on a reversing mill.
Compared with the other production steps, break-down rolling has not been studied
extensively. One of the reasons for this is the absence of a laboratory technique
that simulates this process accurately. During this work the Sheffield Mill for
Aluminium Roughing at Temperature (SMART) was developed and it was proven
that SMART can be used to simulate industrial break-down rolling. Furthermore, the
data generated from SMART have been used to validate and refine a model from
the literature. This model (developed at NTNU in Norway) predicts the evolution of
the recrystallised fraction, the grain size and certain texture components throughout
a multi-pass rolling operation. lt is shown here that the model predictions show a
reasonable agreement with the results from SMART. Using the present
experimental data, a set of recommendations to improve the model has been
derived.
Apart from the microstructural data, the experiments on SMART were also used to
model the lateral spread that occurs during laboratory rolling. A new model is
proposed that shows a better performance compared with the models that are
available from the literature.
The present work was carried out on AA3104 (AI-1Mn-1Mg) which is mostly used
for the production of beverage cans.