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Electron beam melting (EBM) is a promising three‐dimensional printing technology for the fabrication of components with high complexity and freedom of structural design. However, the major limit for its wider industrial applications is to preclude manufacturing the large‐scale part in one piece. It is because the part size is largely limited by the enclosed vacuum chamber. An alternative option is the use of welding to join EBM‐processed subparts together. In this study, fatigue crack growth (FCG) rate and high‐cycle fatigue (HCF) performance were measured for both EBM‐processed Ti–6Al–4V alloy and its welded joints. An X‐parameter model was then proposed to correlate stress amplitude and geometric parameters (location, size, and shape) of the critical defects at the crack origins with fatigue life. Research framework and result from this work are expected to serve a reference for the defect‐based fatigue life evaluation of welded parts individually manufactured using additive manufacturing. [ABSTRACT FROM AUTHOR]

• The tensile properties of WAAM 2219 over the temperature range (77 ∼ 573 K) were obtained. • The effects of temperature on void nucleation and evolution were explored by a combination of SEM and EBSD observations, and X-ray micro computed tomography. • The effects of temperature on deformation and fracture mechanisms were investigated. • True stress and strain responses over a wide temperature range (77 ∼ 523 K) and strain rate range (0.0001 ∼ 0.01 s−1) were predicted using an improved Arrhenius constitutive model. This work investigates the deformation and damage mechanisms of wire + arc additive manufacturing (WAAM) 2219 aluminum alloy over a broad temperature range of 77 K to 523 K. It is found that the strength and work hardening rate increase significantly with decreasing the temperature from 523 K to 77 K, and the fracture mode transitions from transgranular fracture mode to a mixture of intergranular and transgranular fracture ones. The large elongations to fracture are very similar for the specimens tested at 77 K and 523 K. This is mainly attributed to higher uniform elongation before necking at 77 K and higher post-necking elongation at 523 K. The cracking of α-Al + θ (Al 2 Cu) eutectics is the primary mechanism of micro-void nucleation, independent of the test temperature. The nucleated micro-voids, along with larger gas pore defects introduced by WAAM, extend along the tensile direction. In addition, as the temperature increases from 77 K to 523 K, the fracture mechanism transitions from high strain localization to extensive necking, leading to higher dislocation density, more grain fragmentation and micro-void nucleation, and more significant grain elongation and void growth. True stress and strain responses over the studied temperature range (77 ∼ 523 K) and strain rate range (0.0001 ∼ 0.01 s−1) are well predicted using an improved Arrhenius-type constitutive model, by considering the activation energy of deformation as a function of temperature. [ABSTRACT FROM AUTHOR]

• The physics-informed machine learning strategy is proposed for fatigue assessment. • Experimental investigations on the 300M-AerMet100 steel specimens are carried out. • Fractographic analysis shows that the internal defects affect fatigue behaviour. • The CDM based fatigue models and ANN model are presented for numerical prediction. Additive manufacturing (AM) has attracted much attention recently for its immanent advantages. Assessment of the fatigue performance for AM treated materials becomes vital for both material science and engineering applications. In this study, we extensively investigate the fatigue performance of AM processed 300M-AerMet100 steel by combining experiments, numerical simulations and machine learning. We conduct experiments to obtain fatigue curves as calibration and to determine the parameters used in the theoretical models. Continuum damage mechanics-based fatigue models are presented and numerically implemented to generate sufficient training data for machine learning. We then employ a multi-layer perceptron neural network model to predict the fatigue life of the AM processed 300M-AerMet100 steel. Experimental results show that there are scatters in the fatigue data, which may be caused by the small cracks induced by the laser cladding process via fractographic analyses. Numerical results show that a good prediction of fatigue life can be achieved by combining the continuum damage mechanics-based fatigue models and the multi-layer perceptron neural network model. This work provides a systematic prediction platform for the fatigue performance of the AM fabricated 300M-AerMet100 steel. [ABSTRACT FROM AUTHOR]

• Based on the critical defects data measured by SEM, a machine learning model is established to predict the fatigue life, and the model is in good agreement with the experimental data. • Use the trained machine learning model to quantitatively analyze the importance of critical defects characteristics on fatigue life and analyze the effect of aspect ratio on fatigue life. • The ML model suggests that circular defects of the same area to be less potent than elongated defects of the same area in terms of the HCF lifetime. Given their preponderance and propensity to initiate fatigue cracks, understanding the effect of processing defects on fatigue life is a significant step towards the wider application of additively manufactured (AM) parts. Here a novel machine learning (ML) based approach has been developed to predict the fatigue life of laser powder bed fused AlSi10Mg alloy. The four most important parameters, treferred to here as the Wu-Withers parameters, were found to be the applied stress and the projected area, location and morphology of the critical defects. It was found that an Extreme Gradient Boosting model was able to predict the fatigue lives with high accuracy with the importance of these characteristics in limiting fatigue life ranked in the order given above. The model was able to predict the very different lives of samples tested parallel and perpendicular to the build direction in terms of these four W-W parameters indicating that microstructure was of minor importance. In particular the large projected area of the defects on the crack plane when testing parallel to the build direction was found to be primarily responsible for the shorter lives observed for this testing orientation. The fatigue lives were adequately predicted by the more general two variable (stress and projected area) Murakami model, and even more closely predicted by an empirical model (using essentially the same four W-W parameters) for which the ML model corroborated the empirical dependences. [Display omitted] [ABSTRACT FROM AUTHOR]