Surface roughness parameter and modeling for fatigue behavior of additive manufactured parts: A non-destructive data-driven approach

Abstract

Metallic additive manufacturing produces undesired surface roughness due to the nature of the layer-by-layer fabrication process. As-built surface roughness can accelerate surface crack initiation and result in an early fatigue failure under cyclic loading. Therefore, the effect of surface roughness on the structural integrity of additive manufactured parts needs to be characterized before they can be used in fatigue-critical applications. In this study, the surface roughness of Ti-6Al-4V specimens fabricated by a laser beam powder bed fusion system is characterized, using a non-destructive data-driven approach, and correlated with their fatigue performance. Using a data-intensive surface topographical investigation, standard surface roughness parameters with statistical robustness are generated. The results confirm that conventional standard surface roughness parameters cannot appropriately represent the correlation between surface roughness and fatigue lives. Accordingly, a hybrid surface roughness parameter consisting of the maximum valley depth (Rv/Sv), the skewness (Rsk/Ssk), and the kurtosis (Rku/Sku) of the profiled lines/areas is suggested to explain the fatigue behavior. The proposed hybrid surface roughness parameter is validated by vertically fabricated flat specimens and diagonally built cylindrical dog-bone specimens to investigate the effect of design and build orientation. Moreover, a fatigue prediction model which considers the surface roughness as a micro-notch has been modified using the proposed hybrid surface roughness parameter and the part’s layer thickness and validated against experimental data.