A new gradient infill design method for material extrusion using density-based topology optimization and G-code extension

Abstract

Gradient infill design (GID) is important in design for additive manufacturing (AM) because it can improve the strength of filled structures. Conventional GID methods aim at product design and use mathematics to formulate the infill geometry; thus, they are complex and require a high level of expertise from the designer. In this work, we propose a GID approach targeting the process planning phase, where density information from topology optimization is used to extend the G-code for printing a part. To verify the advantages of this method, we programmed an algorithm to extend G-code, produced samples, and performed displacement-force bending tests.