Self-support structure topology optimization for multi-axis additive manufacturing incorporated with curved layer slicing

Multi-axis additive manufacturing significantly surpasses traditional 3-axis systems by utilizing multiple axes of motions that constructs complex three-dimensional structures with reduced need of supports. However, process planning for the curved layer slicing determines the interactions between the part and supports, and consequently, self-support topology optimization requires a numerically tractable process planning algorithm to derive the sensitivities, which however, has yet been achieved. To fill the gap, we develop a structural topology optimization method for multi-axis additive manufacturing, which features in achieving the self-support effect by deeply incorporating the curved layer slicing. Specifically, a process scalar field is generated on top of a domain of pseudo-densities by solving a heat diffusion equation and a Poisson equation, through which the geodesics included in the scalar field facilitate the curved layer slicing and any geometric information about the layers are derivable on the pseudo-densities because of the tractable numerical processing routine. Then, self-support constraints for multi-axis additive manufacturing can be established by measuring the curved layer normals and the part boundary gradients. Coupled with the density variables for topology optimization, our proposed method could concurrently optimize the part structure and its curved slicing pattern, maximizing the structural physical performance while eliminating the need of supports. Finally, we validated and discussed the effectiveness of our method through a series of numerical tests and provided a workflow to show the strong correlation between our optimized results and the actual spatial paths.