Agile flexible smart manufacturing of innovative aluminium products with reduced CO2 footprint

Offentlig prosjektsammendrag

The project is established for advancing capabilities of BENTELER Automotive Raufoss AS (BAR) through innovations and novel technologies to improve existing processes and product portfolio as well as creating novel products for new automotive trends, triggered by steadily growing e-mobility demands for more aluminium components in the future cars as well as stringent sustainability goals. The project is addressing different aspects of anticipated innovations which leads to further novel breakthrough technologies. BAR is cooperating within the scope of this project with the renowned R&D institutes and providers of enabling technologies as well as other manufacturing companies with joint but not necessarily competing ambitions. The project develops innovative solutions for production of Next-generation Crash Management Systems, the rapidly growing e-mobility and the BEV market. The latter is expected to drastically change the use of aluminium in cars. Aluminium is light weight, has good impact protection properties and is 100% recyclable in an infinitive loop, provided the alloy and trace elements are managed properly. The challenges associated with this opportunity and the European Green Deal Initiatives are addressed in this project in cooperation with a strong consortium. Traditional OEMs as well as emerging companies like Sony, Google are entering the market and requiring cost-effective, small serial, and highly flexible production. The project meets these challenges by incorporating flexibility and adaptively in the production process through digitally advanced adaptive tool and processes, and agile control systems. The project provides data-driven methods for a holistic manufacturing variation management, improving process capability, reducing variations in the production. The project utilises a large number of production data providing decision support, monitoring, controlling of multi-process manufacturing variations through novel data management.