Topology optimization parallel-computing framework based on the inherent strain method for support structure design in laser powder-bed fusion additive manufacturing

Abstract

In this work, a topology optimization parallel-computing framework is developed to design support structures for minimizing deflections in Laser Powder-bed Fusion produced parts. The parallel-computing framework consists of a topology optimization model and an Inherent Strain Method (ISM) model. The proposed framework is used to design stiffer support structures to reduce the before and after-cutting deflections in printed cantilevers. Gravity load and residual stresses calculated from ISM are applied in the topology optimization model. The optimized results were printed and analyzed for validating the effectiveness of the proposed model. Experimental results show that the optimized supports can achieve over 60% reduction in part deflection as well as over 50% material usage reduction compared to the default support structure. In addition, ISM also was used to predict the part deflections and shows good agreement (average error of 6%) between the experimental and simulated results. Lastly, the multi-node parallelization of the proposed framework showed ~ 5 times speedup compared to a single-node implementation.