Bridging Between Topology Optimization and Additive Manufacturing via Laplacian Smoothing

Abstract

The potential of additive layer manufacturing (ALM) is high, with a whole new set of manufacturable parts with unseen complexity being offered. Moreover, the combination of topology optimization (TO) with ALM has brought mutual advantages. However, the transition between TO and ALM is a nontrivial step that requires a robust methodology. Thus, the purpose of this work is to evaluate the capabilities of adopting the commonly used Laplacian smoothing methodology as the bridging tool between TO and ALM. Several algorithms are presented and compared in terms of efficiency and performance. Most importantly, a different concept of Laplacian smoothing is presented as well as a set of metrics to evaluate the performance of the algorithms, with the advantages and disadvantages of each algorithm being discussed. In the end, the proposed mutable diffusion Laplacian algorithm is presented and exhibits less volume shrinkage and shows better preservation of some geometrical features such as thin members and edges. Moreover, a new volume constraint is presented, decreasing the resulting structural changes in the presented geometry and improving the final mesh quality.