ADAPT2030, Adaptive lifecycle design by applying digitalization and AI techniques to production - Adapt 2030



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Deviation occurs in all steps of a product lifecycle; e.g., production, transport and operations. Today, many manufacturers have access to an enormous amount of data from the whole lifecycle, which is under-utilised. Despite proven digital and AI (artificial intelligence) techniques, and lifecycle engineering (LCE) methods, the application of these in combination in industrial practice is scarce. Continuously reducing deviations both reactively and proactively with a lifecycle perspective will have a significant economic and environmental gain.

Linköping University and professor Tomohiko Sakao is the consortium leader and LiU is provide leading expertise on LCE. Mälardalen University and professor Peter Funk leader of the MDU team providing leading expertise on AI, ML, big data and innovation.

The project will develop a method and two demonstrators as “proof of concept” showin the process and value of lifecycle digitalization, by applying AI techniques to data for reducing deviations. Two value chains will be addressed; gas turbines and production machines. The data related to deviation collected from the full lifecycle will be used to derive insights for continuous improvements including design of next-generation products.

The major expected impacts for industry will be improved cost efficiency as well as capacity and competitiveness concerning the next level of digitalization with strategic use of AI. Environmental performance including resource efficiency will also be increased. The Swedish National Road and Transport Research Institute is providing leading expertise in transport, mobility and logistic related issues. Two large firms will provide cases that together cover a wide range of value chains for production: Siemens Energy and Volvo Construction Equipment. In addition, a number of companies are providing leading edge expertis to the demonstrators including Addiva and Imagimob.

Peter Funk, Professor

Room: U1-126
Phone: +46-21-103153