Integrated topology optimization design and process planning for additive manufacturing

Abstract

Industry 4.0 demands that the systems and processes in today’s product design and manufacturing not just be automated, but to be robust and containing many feedback mechanisms which enables it to be self-correcting. The hypothetical upcoming Industry 5.0 promises on demand and personalized products which this thesis aims to take a step in the direction of. It is proposed that an integrated and optimized process for structural topology optimization and subsequent additive manufacturing is possible for automated design and manufacturing starting from its problem definition. An improvement on the benchmarked topology optimization methods is shown which allows the user control over the optimization’s convergence characteristics which is then further studied to find a robust set of optimization parameters. The resulting topology of the structure is then analyzed for its optimal printing orientation based on a custom-made algorithm which minimizes manufacturing costs. Furthermore, the structure is then sliced for instruction generation of layer-based manufacturing techniques in a novel fashion which also serves to provide feedback of the manufacturing process planning to the topology optimization design stage.