Artificial neural networks to optimize the extrusion of an aluminium alloy

Abstract

Extrusion of aluminium alloys is a complex process which depends on the characteristics of the material and on the process parameters (initial billet temperature, extrusion ratio, friction at the interfaces, die geometry etc.). The temperature profile at the die exit, largely influences microstructure, mechanical properties, and surface quality of an extruded product, consequently it is the most important parameter for controlling the process. In turn the temperature profile depends on other process variables whose right choice is fundamental to avoid surface damage of the extruded product. In the present work, two neural networks were implemented to optimize the aluminium extrusion process determining the temperature profile of an Al 6060 alloy (UNI 9006/1) at the exit of induction heater (ANN1) and at the exit of the die (ANN2). The three-layer neural networks with Levemberg Marquardt algorithm were trained with the experimental data from the industrial process. The temperature profiles, predicted by the neural network, closely agree with experimental values.