Analysing the effect of defects on stress concentration and fatigue life of L-PBF AlSi10Mg alloy using finite element modelling

Abstract

Additive manufacturing (AM) is a developing manufacturing technology, which provides excellent attributes compared to other manufacturing techniques. However, one of the critical challenges is the presence of defects that hinder the mechanical properties of the parts, particularly the fatigue life. Experimental understanding of fatigue is a cumbersome process. Therefore, numerical prediction based on specified conditions (such as porosity and applied load) can be an alternative to experimental analysis at the design stage of AM parts. In this study, elastic–plastic finite element analysis (FEA) is performed to obtain the stress distribution around pores, and their resultant effect on fatigue life for L-PBF (laser powder bed fusion) produced AlSi10Mg alloy samples. The stress field is calculated for both single and multiple pore models, where stress concentration is evaluated as a function of the pore’s location and its size. It is found that both pore location and size affect the stress field; however, location effects dominate over pore size. For the same remote applied stress level, the stress concentration around the pore increases with an increase in pore size, and the local maximum stress occurs near the pores that are closest to the surface. The current study also evaluates the relative effect of porosity fraction, average pore size, and location. It is found that the magnitude and sensitivity of stress concentration are hierarchically controlled by porosity location, pore size, and porosity density. A multi-scale finite element (FE) model is proposed based on inherent porosity data measured using Computed Tomography (CT) to predict overall fatigue life. The fatigue cycles are calculated using the rainflow counting algorithm in FE Safe using the stress–strain data obtained from the proposed FEA model. Using the proposed model, it is possible to generate S–N curves for any loading condition for a given porosity condition (porosity density and average pore size). The S–N curve results obtained from the FE model are compared to the experimental observations. The predicted fatigue life shows approximately 5% error with experimental results at high stress loading conditions. However, the proposed model overpredicts the fatigue life at low stress loading by almost 30%.

Anisotropic Mechanical and Microstructural Properties of a Ti-6Al-7Nb Alloy for Biomedical Applications Manufactured via Laser Powder Bed Fusion

“…The increased fatigue behavior of 90° as-built Ti-6Al-7Nb could attributed to the effects of defects on the fatigue performance as reported in the literatu [37,47,75]. Defects serve as nucleation points for fatigue cracks due to the local stress c centration [85]. This conclusion needs to be correlated with fracture surface analysis.…”

“…With increasing crack propagation, the effective cross-sectional area decreases and leads to the final residual fracture surface at constant stress amplitudes. The crack propagation area generally reveals PBF-LB/M typical defects like cracks and different types of pores independent of the build orientation (Figure 10g-i) [85]. Examination of crack nucleation reveals local internal defects such as pores to be potential areas for cracks under fatigue loading.…”

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

“…Such a defect may originate from in-service operations [84,85], manufacturing [65], or material processing [86], exerting substantial influence on mechanical performance by acting as regions of stress concentration and crack initiation. Therefore, this case study is designed to analyze the inhomogeneous distribution of stress around a pore which has been commonly observed in structural components [87][88][89], especially in the case of additively manufactured parts [71,[90][91][92]. It is worth mentioning that manufacturing defects in additive processes can be of various shapes [93][94][95][96][97]; however, in our current models, we have primarily considered spherical pores.…”

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

“…The significance of stress concentration analysis extends to many engineering disciplines such as automotive [11], aerospace [12], construction [13], biomechanics [14], etc. Significant effort has been made within the scope of manufacturing to identify the impact of pores or defects on the performance of printed parts [15]. Smith et al [16] studied the effects of large lack of fusion defects in additively manufactured 304 l stainless steel which proved to significantly reduce the tensile strength, ductility, and fatigue life of the material.…”