Sandia National Laboratories
Canvas Category Consultancy : Research : National
Sandia National Laboratories is the nation’s premier DOE science and engineering lab for national security and technology innovation. Our team of scientists, engineers, researchers, and business specialists apply their knowledge and skill toward delivering cutting-edge technology in an array of areas. Across our main sites in Albuquerque, NM, and Livermore, CA, our research ranges from nuclear defense and homeland and global security to innovative work in biotechnology, environmental preservation, energy, and cyber security.
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Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring
Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.
Intel builds world’s largest neuromorphic system
The system, code-named Hala Point, has initially been deployed at Sandia National Laboratories, and uses Intel’s Loihi 2 processor. It is intended to support research into future brain-inspired artificial intelligence (AI), as well as tackling challenges that are related to the efficiency and sustainability of today’s AI.
Hala Point is the first large-scale neuromorphic system to demonstrate state-of-the-art computational efficiencies on mainstream AI workloads. Characterization shows it can support up to 20 petaops, with an efficiency exceeding 15 trillion 8-bit operations per second per watt (TOPS/W) when executing conventional deep neural networks.
NREL To Lead New Lab Consortium To Enable High-Volume Manufacturing of Electrolyzers and Fuel Cells
The Roll-to-Roll (R2R) Consortium is a new national laboratory consortium with a mission to advance efficient, high-throughput, and high-quality manufacturing methods and processes to accelerate domestic manufacturing and reduce the cost of durable, high-performance proton exchange membrane fuel cell and electrolyzer systems.
The R2R Consortium is led by the National Renewable Energy Laboratory (NREL) and includes Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL), Lawrence Berkeley National Laboratory, and Sandia National Laboratories.
High-throughput manufacturing of fuel cells and water electrolyzers is critical for achieving widespread deployment of low-cost, clean hydrogen technologies. Roll-to-roll manufacturing of materials can increase efficiency, reduce material waste, and improve cost, but there are challenges related to materials synthesis, coating, drying, and quality control that need to be addressed to scale up these processes for industry adoption.