Determination of scrap/supply probability curves for the mechanical properties of aluminium alloys in hot extrusion using a neural network-like approach

Authors:
Iztok Peruš;Milan Terčelj;Goran Kugler
Affiliations:
Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, SI-1000 Ljubljana, Slovenia;Department of Materials and Metallurgy, University of Ljubljana, Aškerčeva cesta 12, 1000 Ljubljana, Slovenia;Department of Materials and Metallurgy, University of Ljubljana, Aškerčeva cesta 12, 1000 Ljubljana, Slovenia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

In this paper a neural network-like approach that accounts for the different uncertainties in the hot extrusion of AA6082 alloys is given. The results, presented in the form of scrap/supply curves, suggest the use of a probabilistic approach in the process of hot extrusion. The proposed approach considers both the epistemic and aleatory uncertainties and takes into account all the available influential input variables. The use of the CAE neural network, which is a special type of probabilistic neural network, is proposed as a powerful tool in the design and partial optimization of the hot-extrusion processes in real, industrial aluminium production. It was found that mechanical properties and the yield can be additionally optimized by reducing the epistemic uncertainties, which consequently requires more accurate measurements and more reliable control of the production processes.