Next generation fatigue crack growth experiments of aerospace materials.

Today's societal challenges require rapid response and smart materials solutions in almost all technical areas. Driven by these needs, data-driven research has emerged as an enabler for faster innovation cycles. In fields such as chemistry, materials science and life sciences, automatic and even autonomous data generation and processing is already accelerating knowledge discovery. In contrast, in experimental mechanics, complex investigations like studying fatigue crack growth in structural materials have traditionally adhered to standardized procedures with limited adoption of the digital transformation. In this work, we present a novel infrastructure for data-centric experimental mechanics in the field of fatigue crack growth. Our methodology incorporates a robust code base that complements a multi-scale digital image correlation and robot-assisted test rig. Using this approach, the information-to-cost ratio of fatigue crack growth experiments in aerospace materials is significantly higher compared to traditional experiments. Thus, serves as a catalyst for discovering new scientific knowledge in the field of structural materials and structures.

Damage Mechanisms and Anisotropy of an AA7010-T7452 Open-Die Forged Alloy: Fatigue Crack Propagation.

The process-microstructure-property relationship of high-strength 7000 series aluminum alloys during fatigue crack propagation (FCP) is highly relevant for safety during the design and service of aircraft structural components. It is scientifically evident that many metallurgical factors affect FCP properties, but partly contradictory or inconclusive results show that the quantitative description of the relationships is still a major challenge among researchers and engineers. Most research focuses on sheet or plate products and investigations lack quantitative information on the process-property relationship between open-die forged thick products and FCP. The present study contributes to this field by investigating the fatigue crack growth behavior of an open-die forged AA7010-T7452 aluminum alloy. Four different forging conditions comprising different characteristic microstructures are comparatively analyzed. The influence of grain size, grain shape, specimen orientation, crystallographic texture, and primary phase particles is investigated. Fractographic analysis reveals different active damage mechanisms during fatigue crack growth. Based on that, the microstructure features relevant to fatigue damage areidentified in each regime of crack growth.

Explainable machine learning for precise fatigue crack tip detection.

Data-driven models based on deep learning have led to tremendous breakthroughs in classical computer vision tasks and have recently made their way into natural sciences. However, the absence of domain knowledge in their inherent design significantly hinders the understanding and acceptance of these models. Nevertheless, explainability is crucial to justify the use of deep learning tools in safety-relevant applications such as aircraft component design, service and inspection. In this work, we train convolutional neural networks for crack tip detection in fatigue crack growth experiments using full-field displacement data obtained by digital image correlation. For this, we introduce the novel architecture ParallelNets-a network which combines segmentation and regression of the crack tip coordinates-and compare it with a classical U-Net-based architecture. Aiming for explainability, we use the Grad-CAM interpretability method to visualize the neural attention of several models. Attention heatmaps show that ParallelNets is able to focus on physically relevant areas like the crack tip field, which explains its superior performance in terms of accuracy, robustness, and stability.

Interface-Mediated Twinning-Induced Plasticity in a Fine Hexagonal Microstructure Generated by Additive Manufacturing.

The grain size is a determinant microstructural feature to enable the activation of deformation twinning in hexagonal close-packed (hcp) metals. Although deformation twinning is one of the most effective mechanisms for improving the strength-ductility trade-off of structural alloys, its activation is reduced with decreasing grain size. This work reports the discovery of the activation of deformation twinning in a fine-grained hcp microstructure by introducing ductile body-centered cubic (bcc) nano-layer interfaces. The fast solidification and cooling conditions of laser-based additive manufacturing are exploited to obtain a fine microstructure that, coupled with an intensified intrinsic heat treatment, permits to generate the bcc nano-layers. In situ high-energy synchrotron X-ray diffraction allows tracking the activation and evolution of mechanical twinning in real-time. The findings obtained show the potential of ductile nano-layering for the novel design of hcp damage tolerant materials with improved life spans.

Pyrometric-Based Melt Pool Monitoring Study of CuCr1Zr Processed Using L-PBF.

The potential of in situ melt pool monitoring (MPM) for parameter development and furthering the process understanding in Laser Powder Bed Fusion (LPBF) of CuCr1Zr was investigated. Commercial MPM systems are currently being developed as a quality monitoring tool with the aim of detecting faulty parts already in the build process and, thus, reducing costs in LPBF. A detailed analysis of coupon specimens allowed two processing windows to be established for a suitably dense material at layer thicknesses of 30 µm and 50 µm, which were subsequently evaluated with two complex thermomechanical-fatigue (TMF) panels. Variations due to the location on the build platform were taken into account for the parameter development. Importantly, integrally averaged MPM intensities showed no direct correlation with total porosities, while the robustness of the melting process, impacted strongly by balling, affected the scattering of the MPM response and can thus be assessed. However, the MPM results, similar to material properties such as porosity, cannot be directly transferred from coupon specimens to components due to the influence of the local part geometry and heat transport on the build platform. Different MPM intensity ranges are obtained on cuboids and TMF panels despite similar LPBF parameters. Nonetheless, besides identifying LPBF parameter windows with a stable process, MPM allowed the successful detection of individual defects on the surface and in the bulk of the large demonstrators and appears to be a suitable tool for quality monitoring during fabrication and non-destructive evaluation of the LPBF process.

Pandora's Box-Influence of Contour Parameters on Roughness and Subsurface Residual Stresses in Laser Powder Bed Fusion of Ti-6Al-4V.

The contour scan strategies in laser powder bed fusion (LPBF) of Ti-6Al-4V were studied at the coupon level. These scan strategies determined the surface qualities and subsurface residual stresses. The correlations to these properties were identified for an optimization of the LPBF processing. The surface roughness and the residual stresses in build direction were linked: combining high laser power and high scan velocities with at least two contour lines substantially reduced the surface roughness, expressed by the arithmetic mean height, from values as high as 30 µm to 13 µm, while the residual stresses rose from ~340 to about 800 MPa. At this stress level, manufactured rocket fuel injector components evidenced macroscopic cracking. A scan strategy completing the contour region at 100 W and 1050 mm/s is recommended as a compromise between residual stresses (625 MPa) and surface quality (14.2 µm). The LPBF builds were monitored with an in-line twin-photodiode-based melt pool monitoring (MPM) system, which revealed a correlation between the intensity quotient I2/I1, the surface roughness, and the residual stresses. Thus, this MPM system can provide a predictive estimate of the surface quality of the samples and resulting residual stresses in the material generated during LPBF.

Semantic segmentation of synchrotron tomography of multiphase Al-Si alloys using a convolutional neural network with a pixel-wise weighted loss function.

Human-based segmentation of tomographic images can be a tedious time-consuming task. Deep learning algorithms and, particularly, convolutional neural networks have become state of the art techniques for pattern recognition in digital images that can replace human-based image segmentation. However, their use in materials science is beginning to be explored and their application needs to be adapted to the specific needs of this field. In the present work, a convolutional neural network is trained to segment the microstructural components of an Al-Si cast alloy imaged using synchrotron X-ray tomography. A pixel-wise weighted error function is implemented to account for microstructural features which are hard to identify in the tomographs and that play a relevant role for the correct description of the 3D architecture of the alloy investigated. The results show that the total operation time for the segmentation using the trained convolutional neural network was reduced to <1% of the time needed with human-based segmentation.

Special Issue: Thermo-Mechanical Behaviour of Structural Lightweight Alloys.

The need to reduce the ecological footprint of (water, land, air) vehicles in this era of climate change requires pushing the limits in the development of lightweight structures and materials [...].

Revealing the Effect of Local Connectivity of Rigid Phases during Deformation at High Temperature of Cast AlSi12Cu4Ni(2,3)Mg Alloys.

The 3D microstructure and its effect on damage formation and accumulation during tensile deformation at 300 °C for cast, near eutectic AlSi12Cu4Ni2Mg and AlSi12Cu4Ni3Mg alloys has been investigated using in-situ synchrotron micro-tomography, complemented by conventional 2D characterization methods. An increase of Ni from 2 to 3 wt.% leads to a higher local connectivity, quantified by the Euler number χ, at constant global interconnectivity of rigid 3D networks formed by primary and eutectic Si and intermetallics owing to the formation of the plate-like Al-Ni-Cu-rich δ-phase. Damage initiates as micro-cracks through primary Si particles agglomerated in clusters and as voids at matrix/rigid phase interfaces. Coalescence of voids leads to final fracture with the main crack propagating along damaged rigid particles as well as through the matrix. The lower local connectivity of the rigid 3D network in the alloy with 2 wt.% Ni permits localized plastification of the matrix and helps accommodating more damage resulting in an increase of ductility with respect to AlSi12Cu4Ni3Mg. A simple load partition approach that considers the evolution of local connectivity of rigid networks as a function of strain is proposed based on in-situ experimental data.

Peritectic titanium alloys for 3D printing.

Metal-based additive manufacturing (AM) permits layer-by-layer fabrication of near net-shaped metallic components with complex geometries not achievable using the design constraints of traditional manufacturing. Production savings of titanium-based components by AM are estimated up to 50% owing to the current exorbitant loss of material during machining. Nowadays, most of the titanium alloys for AM are based on conventional compositions still tailored to conventional manufacturing not considering the directional thermal gradient that provokes epitaxial growth during AM. This results in severely textured microstructures associated with anisotropic structural properties usually remaining upon post-AM processing. The present investigations reveal a promising solidification and cooling path for α formation not yet exploited, in which α does not inherit the usual crystallographic orientation relationship with the parent ß phase. The associated decrease in anisotropy, accompanied by the formation of equiaxed microstructures represents a step forward toward a next generation of titanium alloys for AM.

Inducing Stable α + ß Microstructures during Selective Laser Melting of Ti-6Al-4V Using Intensified Intrinsic Heat Treatments.

Selective laser melting is a promising powder-bed-based additive manufacturing technique for titanium alloys: near net-shaped metallic components can be produced with high resource-efficiency and cost savings [...].

An Assessment of Subsurface Residual Stress Analysis in SLM Ti-6Al-4V.

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.

Non-negative matrix factorization for the near real-time interpretation of absorption effects in elemental distribution images acquired by X-ray fluorescence imaging.

Elemental distribution images acquired by imaging X-ray fluorescence analysis can contain high degrees of redundancy and weakly discernible correlations. In this article near real-time non-negative matrix factorization (NMF) is described for the analysis of a number of data sets acquired from samples of a bi-modal α+ß Ti-6Al-6V-2Sn alloy. NMF was used for the first time to reveal absorption artefacts in the elemental distribution images of the samples, where two phases of the alloy, namely α and ß, were in superposition. The findings and interpretation of the NMF results were confirmed by Monte Carlo simulation of the layered alloy system. Furthermore, it is shown how the simultaneous factorization of several stacks of elemental distribution images provides uniform basis vectors and consequently simplifies the interpretation of the representation.

3D elemental sensitive imaging using transmission X-ray microscopy.

Determination of the heterogeneous distribution of metals in alloy/battery/catalyst and biological materials is critical to fully characterize and/or evaluate the functionality of the materials. Using synchrotron-based transmission x-ray microscopy (TXM), it is now feasible to perform nanoscale-resolution imaging over a wide X-ray energy range covering the absorption edges of many elements; combining elemental sensitive imaging with determination of sample morphology. We present an efficient and reliable methodology to perform 3D elemental sensitive imaging with excellent sample penetration (tens of microns) using hard X-ray TXM. A sample of an Al-Si piston alloy is used to demonstrate the capability of the proposed method.

A comparative study of high resolution cone beam X-ray tomography and synchrotron tomography applied to Fe- and Al-alloys.

X-ray computed tomography (XCT) has become a very important method for non-destructive 3D-characterization and evaluation of materials. Due to measurement speed and quality, XCT systems with cone beam geometry and matrix detectors have gained general acceptance. Continuous improvements in the quality and performance of X-ray tubes and XCT devices have led to cone beam CT systems that can now achieve spatial resolutions down to 1 µm and even below. However, the polychromatic nature of the source, limited photon flux and cone beam artefacts mean that there are limits to the quality of the CT-data achievable; these limits are particularly pronounced with materials of higher density like metals. Synchrotron radiation offers significant advantages by its monochromatic and parallel beam of high brilliance. These advantages usually cause fewer artefacts, improved contrast and resolution.Tomography data of a steel sample and of two multi-phase Al-samples (AlSi12Ni1, AlMg5Si7) are recorded by advanced cone beam XCT-systems with a µ-focus (µXCT) and a sub-µm (nano-focus, sub-µXCT) X-ray source with voxel dimensions between 0.4 and 3.5 µm and are compared with synchrotron computed tomography (sXCT) with 0.3 µm/voxel. CT data features like beam hardening and ring artefacts, detection of details, sharpness, contrast, signal-to-noise ratio and the grey value histogram are systematically compared. In all cases µXCT displayed the lowest performance. Sub-µXCT gives excellent results in the detection of details, spatial and contrast resolution, which are comparable to synchrotron-XCT recordings. The signal-to-noise ratio is usually significantly lower for sub-µXCT compared with the two other methods. With regard to measurement costs "for industrial users", scanning volume, accessibility and user-friendliness sub-µXCT has significant advantages in comparison to synchrotron-XCT.