Defect-Driven topology optimization for fatigue design of additive manufacturing structures: Application on a real industrial aerospace component. (December 2022)

• Record Type: Journal Article
• Authors: Boursier Niutta, C.; Tridello, A.; Barletta, G.; Gallo, N.; Baroni, A.; Berto, F.; Paolino, D.S.
• Is Part Of: Engineering failure analysis. Volume 142(2022)
• Publication Date: 2022-12
• Subjects: Topology Optimization -- Fatigue -- Defect Population -- Additive Manufacturing; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106737
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Defect-driven topology optimization for fatigue design of AM components. Defect size distribution accounted to limit the maximum principal stress. Iterative procedure as the defect distribution depends on the final volume. Additional 15% of mass saving by applying the proposed formulation to an aerospace component. Abstract: In this paper, a generalized formulation of defect-driven topology optimization (TO) for fatigue design, named TopFat, is proposed, where the first principal stress, that causes the crack propagation, is limited in accordance with the defect size distribution of the additive manufacturing (AM) process. As the largest defect depends on the final volume of the component, which changes according to the topology, an iterative procedure is adopted to avoid volume constraints in the optimization problem. The procedure is applied to an actual aerospace bracket component and its strength and limitations are discussed in comparison with the TO formulations presented in the literature. Results show that considering the defect population significantly affects the final topology, while leading to a feasible optimum. Further, even though the computational time increases, an additional 15% of mass saving is achieved by adopting the proposed iterative procedure with respect to a volume constraint-based formulation of TopFat .