Alessandro Cattaneo’s research while affiliated with Los Alamos National Laboratory and other places

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, the extreme light environment and high speed dynamics of metallic melt pools have made this a difficult environment in which to make measurements. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when a measured quantity exceeds a threshold resolution. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. Event-driven driven imagers stand out from conventional imager technology in that they have a very high dynamic range of approximately 120 dB. Conventional 8 bit imagers only have a dynamic range of about 48 dB. This high dynamic range makes them a good candidate for monitoring manufacturing processes that feature high intensity light sources/generation such as metallic additive manufacturing and welding. In addition event based imagers are able to capture data at timescales on the order of 100 {\mu}s, which makes them attractive to capturing fast dynamics in a metallic melt pool. In this work we demonstrate that event-driven imagers have been shown to be able to observe tungsten inert gas (TIG) and laser welding melt pools. The results of this effort suggest that with additional engineering effort, neuromorphic event imagers should be capable of 3D geometry measurements of the melt pool, and anomaly detection/classification/prediction.

Commercial Radio Frequency Identification (RFID) systems have demonstrated a reliable performance for inventory automation and tracking of assets in several industry contexts. However, their feasibility and performance in complex nuclear environments is widely unknown. The ability of RFID systems to properly perform in these environments can be highly affected by multipath propagation problems created by reflective elements, reducing the reliable reading of tags affixed to nuclear material metal enclosures. Thus, understand the physical limits of these antenna devices for automated inventory is a crucial step to select the appropriate RFID tag antennas and methods for tag attachment to achieve reliable and high-performance inventory and tracking systems. In this work, we explore the applicability of finite element method solvers to simulate the dynamics of ultra-high-frequency (UHF) commercial off-the-shelf RFID components to create radio-frequency transport models on different timescales to study the performance of antenna tag matching in simulated nuclear environments. Our goals are to determine how to affix tags to nuclear material containers to maximize the likelihood of tag matching. Also, we aim at characterizing the robustness of these tag antennas to frequency detuning effects potentially caused by the number of metallic surfaces present in these environments. We use a 3D electromagnetic simulation software to verify frequency responses in terms of antenna radiation and scattering patterns, evaluate how much power is reflected and the coupling between different elements in complex nuclear environments with multiple metallic enclosures. The ultimate goal is to precisely define a set of design requirements for RFID tag antennas that could allow to a proper use in the tracking of metallic containers for nuclear material accountability.

Digital twins are virtual representations of real-world structures that can be used for modeling and simulation. Because of digital twins’ ability to simulate complex structural behaviors, they also have potential for structural health monitoring (SHM) applications. Video-based SHM techniques are advantageous due to the lower installation/maintenance costs, analysis in high-spatial resolution, and its non-contact monitoring features. Both digital twins and video-based techniques hold particular interest in the fields of non-destructive evaluation, damage identification, and modal analysis. An effective use of these techniques for SHM applications still poses several challenges. Neural radiance fields (NeRFs) are an emerging and promising type of neural network that can render photorealistic novel views of a complex scene using a sparse data set of 2D images. Originally, NeRF was designed to capture static scenes, but recent work has extended its capability to capture dynamic scenes which has implications for medium and long-term SHM. However, to date, most NeRFs use frame-based images and videos as input data. Frame-based video monitoring approaches result in redundant information derived from the fact that, for structural dynamics monitoring, only a small number of active pixels record the actual dynamical changes in the structure, resulting in intensive computational loads for data processing and storage. A promising alternative is event-based imaging, which only records pixel-wise changes on the illumination of a scene. Event-based imaging creates a sparse set of data, while accurately capturing the dynamics. The work proposes a method to extract the dynamics of a structure using a generated digital twin. Using Unreal Engine 5, digital twins of rigid and non-rigid structures were generated. The digital twin model was then used along with an event-based camera simulator to generate event-based data. A frequency analysis framework was then developed to extract the modal information on the structure. Validation was performed on a structure of known dynamics using event-based cameras.

In recent years imager-based approaches to structural dynamics measurements have gained increasing interest. Imager-based approaches to measurement have a number of attractive properties including being able to monitor large areas at a relatively high spatial resolution with a relatively small number of imagers. The stand-off monitoring capabilities of imagers are also highly attractive for many structural dynamics and structural health monitoring applications, particularly those in dangerous, inaccessible, extreme, and high temperature environments. For their advantages though, a number of challenges must be addressed when using imager-based techniques for measuring the deformation and motion of structures. There are issues of lighting conditions that can potentially vary during the measurement period, due to movement of the sun or lights in a facility being turned on and off. In addition, video measurement of dynamic structures also requires making a number of choices associated with the measurement setup including location of the imagers, focal lengths of lenses, numberof- pixels, lens characteristics, framerates, shutter speeds, ISO, aperture settings, planes of focus, and depth-of-field. There can potentially be effects such as specular reflections or shadows which might complicate downstream data processing. The large number of parameters associated with imager measurements make them very flexible, but it also can mean that setting up imager measurements can take substantial time even under controlled laboratory conditions. The complication associated with imager measurements suggests that highfidelity, photorealistic rendering tools that are capable of capturing the interactions between structural dynamics, light transport, and the measurement process at the imaging plane are needed. To date, substantial work has been done by the computer graphics community to develop photorealistic rendering tools which are becoming increasingly accessible. However, in structural dynamics we often care about sub-pixel motion and it is not understood whether current techniques for modeling effects such as motion blur have sufficient fidelity for structural dynamics. Furthermore, photorealistic video renders can take a large amount of time to complete, and given current techniques do not allow for simple changes such as frame-rate/shutter speed in post processing without redoing the entire render. In this work we present an approach based on digital coded exposures for forming frames of dynamic scenes, that allows for the framerate and shutter speed to be changed in post-processing. The proposed approach is inspired by the physical operation of conventional cameras. In addition, the digital coded exposure proposed approach suggests theoretical alternatives to data capture that could potentially be used to control motion blur properties on a per-pixel basis.