Uppsala University

Data-driven optimization for more resource-efficient 3D-printing processes

  • Circularity and Replacement
  • Design & Modelling
Academic project

Research area

Additive manufacturing (AM) is a relatively new technology, and there is still not an in-depth understanding of the correlation between process parameters and the resulting microstructure for many materials. The aim of this project is to enhance our understanding of these process-structure relationships by developing and applying machine learning models to data gathered for light-weight, Mg-based alloys.

Sustainability aspects

Learning about the relationship between processing and material microstructure will allow us to train models that can speed up the AM material development processes, hence requiring less material, energy and other resources. This will in particular be applied to light-weight alloys, to, in the future, improve their properties and expand their use for fuel- and emission-saving purposes. There is hence a dual sustainability aspect.

researcher photo

Uppsala University

Cecilia Persson



Uppsala University

Dave Zachariah



Explore projects under the Wise program

WISE drives the development of future materials science at the international forefront. The research should lead to the development of sustainable and efficient materials to solve some of today's major challenges, primary sustainability. On this page you can read more about our research projects.

Explore projects