Voestalpine uses around 2,500 sawing, grinding and milling machines in its industrial plants. These consume approximately 21 GWh per year, corresponding to the electricity consumption of around 4,750 average Austrian households.

“Metal processing machines on the factory floor fulfil a variety of tasks. At present, the question of which machine should be used for which task and when has yet to be definitively resolved,” states Gerhard Friedrich, head of the SAELING project at the Department of Artificial Intelligence and Cybersecurity at the University of Klagenfurt. “We need to take many factors into account in order to develop strategies for sawing, grinding and milling in these kinds of workshops in a way that saves energy and resources wherever possible. Considering and simulating these factors along with their full impact is beyond the capabilities of human reasoning. In particular, the behaviour of these machines cannot be described with sufficient precision, but rather it has to be learned for the purpose of optimisation.”

Artificial intelligence methods are now set to significantly reduce energy consumption thanks to more efficient use, as Gerhard Friedrich goes on to explain: “Approaches such as reasoning, optimisation and machine learning will be put to use.”

The results from SAELING should facilitate analogue savings in other production areas. In addition to CO2 emissions, it should also be possible to reduce lubricant consumption, for example. It is intended that the tools developed in the project will be adaptable and can be extended to other areas of application, e.g. at SAELING’s partner Siemens.

For further information visit our HP SAELING and the Magazine Hi!Tech. of Siemens, one of our cooperation-partners.