A fast method of material, design and process eco-selection via topology optimization, for additive manufactured structures

We propose an innovative approach to minimize the greenhouse gas impacts of additive manufactured structures over their entire life cycle. The novelty of our method lies in its simultaneous optimization of material selection, process selection, and design optimization. To fully leverage the potential benefits of additive manufacturing, we use topology optimization and compile a comprehensive database of printed materials and printing processes, which we share with the wider community. To account for the complex interdependence between materials and processes, our method employs a pairing system, which we efficiently reduce using topology optimization properties and a generalized form of Ashby indices. To enhance computational efficiency, we employ a meta-model. We validate our proposed method through successful testing on an aeronautical case and a pedestrian bridge, demonstrating its robustness even in the presence of environmental data uncertainty. The optimal material-process pair for the aeronautical structure is the cobalt-based super-alloy with the LENS process. Despite this pair having the highest material and processing emissions, the resulting lighter part lowers the use phase emissions. It appears that precise mechanical data is needed for the method to give accurate results: a 20% drop of Young's modulus totally disrupts the material-process pair ranking.