While the volumetric energy density is commonly used to qualify a process parameter set, and to quantify its influence on the microstructure and performance of additively manufactured (AM) materials and components, it has been already shown that this description is by no means exhaustive. In this work, new aspects of the optimization of the selective laser melting process are investigated for AM Ti-6Al-4V. We focus on the amount of near-surface residual stress (RS), often blamed for the failure of components, and on the porosity characteristics (amount and spatial distribution). First, using synchrotron x-ray diffraction we show that higher RS in the subsurface region is generated if a lower energy density is used. However, we show that laser de-focusing and sample positioning inside the build chamber also play an eminent role, and we quantify this influence. In parallel, using X-ray Computed Tomography, we observe that porosity is mainly concentrated in the contour region, except in the case where the laser speed is small. The low values of porosity (less than 1%) do not influence RS.

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Ti-6Al-4V bridges were additively fabricated by selective laser melting (SLM) under different scanning speed conditions, to compare the effect of process energy density on the residual stress state. Subsurface lattice strain characterization was conducted by means of synchrotron diffraction in energy dispersive mode. High tensile strain gradients were found at the frontal surface for samples in an as-built condition. The geometry of the samples promotes increasing strains towards the pillar of the bridges. We observed that the higher the laser energy density during fabrication, the lower the lattice strains. A relief of lattice strains takes place after heat treatment.

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The greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Traditionally, these have been attained by manually annotating a few slices for 2D DCNNs. However, complex multiphase microstructures would presumably be better segmented with 3D networks. However, manual segmentation labeling for 3D problems is prohibitive. In this work, we introduce a method for generating synthetic XCT data for a challenging six-phase Al–Si alloy composite reinforced with ceramic fibers and particles. Moreover, we propose certain data augmentations (brightness, contrast, noise, and blur), a special in-house designed deep convolutional neural network (Triple UNet), and a multi-view forwarding strategy to promote generalized learning from synthetic data and therefore achieve successful segmentations. We obtain an overall Dice score of 0.77. Lastly, we prove the detrimental effects of artifacts in the XCT data on achieving accurate segmentations when synthetic data are employed for training the DCNNs. The methods presented in this work are applicable to other materials and imaging techniques as well. Successful segmentation coupled with neural networks trained with synthetic data will accelerate scientific output.

The aim of this experimental study was to quantify the changes that occur to the residual-stress state and the near-surface work-hardened zone as a function of depth in a shot-peened UDIMET 720Li Ni superalloy following solely thermal exposure and in combination with strain-controlled fatigue loading at two strain amplitudes. Residual-stress measurements were performed using the sin 2 method on a laboratory X-ray diffractometer as a function of depth by successive electrochemical removal of material. It was found that the as-peened variation in diffraction-peak width (X-ray) with depth correlated well with the recorded hardness profile. A hardness increase in excess of 50 pct was recorded close to the surface. Regarding solely thermal exposure, it was found that the near-surface compressive stresses were relieved to some extent at all temperatures (350 °C to 725 °C) after short-term exposure, being reduced by up to 50 pct at the highest temperatures (650 °C to 725 °C). The isothermally fatigued samples strained at 0.6 pct amplitude displayed similar stress-relaxation behavior to the thermally loaded samples, indicating that at such small strains, stress relaxation is controlled predominantly by thermal relaxation processes. In contrast, stress relaxation following strain-controlled fatigue at 1.2 pct strain is governed by a combination of thermal and mechanical processes. The mechanically induced relaxation component is anisotropic, being significantly greater along the loading axis than transverse to it. Unsurprisingly, with increasing temperature, the thermal contribution to stress relaxation becomes increasingly important and the degree of in-plane anisotropy of stress relaxation lessens.