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.