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Dr Varužan Kevorkijan

Managing Director at Impol Group


Varužan Kevorkijan is the doctor of the materials science with more than 25 years of experience in aluminium industry and industrial R&D.

In Impol Aluminium Group, as the managing Director of Quality and Research, he  is currently fully responsible for the quality control and certification.

His industrial and academicals R&D activities are focused on the advanced aluminium recycling technologies, the development of new, recycling friendly aluminium alloys, the fabrication and characterization of aluminium-based composites, the stochastic and cognitive modelling of advanced materials, the development of advanced continuous casting technologies, the surface engineering of light metals and composites, the development of novel forming technologies and the advanced materials characterisation.

He is an author of more than 300 scientific and technical papers and the member of numerous international professional societies.

Abstract - Data-driven Industrial Modelling of Future Wrought Aluminium Alloys

In the successful development and commercialization of future wrought aluminium alloys and the corresponding end-products, it is necessary to understand and to be able to predict how the required combination of properties is affected by the different available processing paths, i.e., the chemical composition of the alloy and the main processing parameters. 

An industrial tool developed within the Impol Aluminium Group for such a modelling of wrought aluminium alloys is OPTIAl – the cognitive computing algorithm for correlating the properties of wrought aluminium alloys, the chemical composition and the processing parameters. As will be reported, the computing methodology is based on the Bayesian inference, which makes the algorithm extremely fast and efficient.   The inductive learning of the algorithm was performed by applying the experimentally confirmed equivalency of different technological paths, able to provide the same combination of properties. In the first step, by practicing the data mining, a data matrix was created, consisting of the results of standard, room-temperature tensile tests and the corresponding technological paths for different production lots of the AA 6110 alloy. Next, the most probable technological path-property correlations were identified. Finally, various standard and some non-standard alloy compositions, derived from the alloy AA 6110, and the processing parameters were cognitively inducted to provide the desired combination of properties.

The validation of the above-described methodology was performed through the regular production of a limited number of cognitively computed alloys and their detailed characterization. It was found that the achieved predictions are with high accuracy, sufficient for most industrial applications.

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