16 Search Results

The failure mechanisms of electrically conductive adhesives (ECAs) in solar modules are difficult to study since the ECA layer is not easily accessible within the module package. In this work, we present two complementary imaging modalities—X-ray radiography and X-ray microcomputed tomography (XCT)—that reveal important morphological features of the ECA within a shingled module. X-ray radiography uses single X-ray projections to provide fast and non-destructive imaging of the shingled interconnection, illuminating the alignment of the ECA relative to the busbars, the size and shape of the ECA, and the presence of voids within it. Through X-ray radiography, we observed, for example,more » that the average void coverage area of ECA segments reduced from 36.7% to 4.4% when an ECA was cured for 60 s prior to module lamination. XCT is a three-dimensional imaging technique that can identify regions in which the ECA makes electrical contact to busbars on cells and regions in which the ECA has cracked, among other features. XCT can also be used to image the individual metal particles within ECA, from which the metal volume fraction of an ECA was found here to be 70.4%. This is a quantity that is not often reported by ECA manufacturers but is important to ensure isotropic conduction. As X-ray projections can be performed non-destructively on full modules, the technique may be used to pinpoint ECA failures in accelerated degradation testing. XCT is complementary and is suited to forensic analysis of failing modules.« less

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along withmore » appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.« less

In situ synchrotron X-ray microtomography was used to characterize the bulk deformation behavior by computing the Poisson's ratio of expanded thermoplastic polyurethane (eTPU) molded bead foams used in footwear midsole during compression. Quantitative data on morphological characteristics were obtained using an iterative image processing workflow. Image correlation on the 4D datasets using DVC was performed to calculate the volumetric and axial strain to estimate the Poisson ratio. Strain maps from DVC showed the influence of variability in ligament thickness distribution on the global mechanical behavior exhibited which dominated the response seen in these bead foams. Lastly, our results showed amore » strong correlation between Poisson ratio and distribution of ligament thickness in foams.« less

The longevity of solar photovoltaic modules depends on the durability and reliability of their components, one of which is the solder bonds in interconnect ribbons. The solder joints experience stresses from thermal cycling and constant elevated temperatures (40 °C-70 °C) in regular field operation leading to thermo-mechanical fatigue and intermetallic compound formation. To study the end-of-life wear-out mechanisms and to obtain activation energy of solder bond degradation, here two field-aged modules from Arizona-a 21-year-old Solarex MSX60 module (with Sn62Pb36Ag2 at the solder joints) and an 18-year-old Siemens M55 module (with Sn60Pb40 at the solder joints)-underwent 800 and 400 modified thermalmore » cycles, respectively. Using three heating blankets, each module had three temperature zones maintained at 85, 95, and 105 °C during the 15-min hot dwell time of the thermal cycle. Cell-level series resistance data obtained from three temperature zones enabled the calculation of activation energy for solder bond degradation for the MSX60 and the M55 modules to be 0.12 eV and 0.35 eV, respectively. From each temperature zone in both modules, busbar-solder samples were obtained, imaged through SEM, and analyzed with energy-dispersive X-ray spectroscopy. In the MSX60 module with traces of Ag in the solder material, phase segregation and growth were primarily observed at high temperatures. For M55 modules without Ag in the solder material, major phase segregation was observed in all temperature zones. The IMC thickness for both modules increased with increasing module temperature. The beneficial effect of Ag in solder material on mitigating solder bond degradation is presented.« less

Understanding mechanical failure, crack propagation, and compressive behavior at the micrometer scale is essential for tailoring material properties for structural performance in cellular materials. Typically, modeling of traditional polymer foam materials is clouded by the lack of control in material morphology and its inherent stochastic structure. Additive manufacturing with sub-micrometer resolution provides a direct path for experimenters to specifically tailor structures needed by modelers to explicitly probe mechanical performance. Using laboratory-based 3D X-ray computed tomographic imaging (CT), the examination of deformation and damage provides a critical path to understand how these soft materials behave. Additionally, synchrotron CT yields realistic informationmore » at higher strain rates to directly validate the robustness of our finite element modeling. For this study, nanolithographic printing was employed to generate a series of engineered lattices with increasing levels of defects through the random removal of ligaments. These structures were mechanically tested and imaged with laboratory-based microCT. Additionally, synchrotron experiments were conducted in which the structures were imaged in 3D at 14 Hz during compression at a 0.4 s^–1 strain rate. These 3D images show the changes in the structure as the ligaments bend, buckle and fracture in real time. This technique provides a robust framework for developing our methodologies and future exploration of engineered structures.« less

Even after nearly a century of extensive use of aluminum alloys in structural applications, our understanding of such precipitation-strengthened materials is far from complete. With the advent of next generation advanced characterization techniques, our ability to probe materials in unique ways and at different length scales has established a new paradigm for devising new pathways to alloy design by engineering materials and tailoring specific properties at the nanoscale. Here, we perform in situ nanomechanical testing in conjunction with synchrotron -based hard X-ray nanotomography to capture initiation and evolution of damage in 3D in Al-Cu alloys. Precipitates in these alloys aremore » seen to exhibit unprecedented localized deformation in compression, which is attributed to novel observations of kinking in these brittle second-phase particles, accompanied with the generation of a fine polycrystalline texture in the adjacent matrix. We observe a size-dependent transition in precipitate deformation behavior that has been thoroughly investigated using a comprehensive correlative approach.« less

A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracymore » of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.« less

Structural materials are subjected to combinations of stress and corrosive environments that work synergistically to cause premature failure. Therefore, studies on the combined effect of stress and corrosive environments on material behavior are required. Existing studies have been performed in two dimensions that are inadequate for full comprehension of the three-dimensional (3D) processes related to stress corrosion cracking (SCC) and corrosion-fatigue (CF) behavior. Recently, x-ray synchrotron tomography has evolved as an excellent technique to obtain the microstructure in 3D. Moreover, being nondestructive in nature, x-ray synchrotron tomography is well suited to study the evolution of microstructure with time (4D, ormore » fourth dimension in time). Furthermore, this article presents our recent 4D studies on SCC and CF of Al 7075 alloys using x-ray synchrotron tomography.« less