Abstract
Process optimization problem of additive manufacturing nowadays is a research hotspot in the field of manufacturing industry. However, parameter uncertainty has not been considered in the past. In this paper, the process optimization methods of additive manufacturing are reviewed, and a novel reliability-centered optimization combining stochastic finite element analysis (SFEA) with particle swarm optimization (PSO) method is proposed and have been explained in details. Finally, the Direct Metal Deposition (DMD) process is used as an example in this paper, and parameters including the layer thickness, heat generation of the melt, scanning speed, as well as the hot bed temperature flux are taking into account. Deformation after the part cools down is the single optimization objective, and the reliability performance is treated as an uncertain constraint in the optimization problem. As a result, the best process parameters of DMD are obtained, and the case study verified the superiority of the proposed method.
Published Version
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