Canvas Category: OEM : Aerospace
At Raytheon Technologies, we’re accelerating ideas to solve some of the world’s biggest challenges by bringing together the brightest, most innovative minds across aviation, space and defense. We form an unrivaled company, with one team coming together across the globe to push the limits of known science and redefine how we connect and protect our world. We are advancing aviation, building smarter defense systems and creating innovations to take us deeper into space.
Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning
Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000 µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. We also introduce for the first time in the AM in situ sensing literature the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the belief of the authors that future studies should adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.
IMPULSE SPACE SECURES $45M IN SERIES A FUNDING ROUND
Impulse Space, Inc. – a leader in the development of in-space transportation services for the inner solar system – today announced it has secured $45 million in its Series A funding round. The round is led by RTX Ventures, the venture capital arm of RTX (NYSE: RTX).
With an oversubscribed funding round, Impulse Space will be continuing its progress with its work in upcoming missions, such as LEO Express-1, a GEO refueling mission and the upcoming mission to Mars. Specifically, this funding will support the development of Impulse’s largest vehicle yet, called Helios. The Helios kick stage enables direct to Geostationary Equatorial Orbit missions, thus bypassing the need for a Geostationary Transfer Orbit.
🖨️ Fortify Secures $12.5 Million in Funding From Investors, Including Lockheed Martin Ventures & RTX Ventures, to Accelerate Growth in Advanced Materials & Additive Manufacturing
Fortify, a leading full-stack materials science and additive manufacturing company, announced today that it has raised $12.5 million in a funding round from investors, including Lockheed Martin Ventures and RTX Ventures, the venture capital arms of Lockheed Martin and Raytheon Technologies, respectively. This strategic investment will enable Fortify to expand its capabilities and accelerate the development of its groundbreaking Digital Composite Manufacturing (DCM) platform.
The future of manufacturing is iterative, collaborative and data-driven
Another key digital transformation practice is integrating artificial intelligence (AI) and machine learning (ML) to automate, or at least streamline and simplify, product development. At Raytheon, teams leverage model-based engineering to predict complex fluid, structural, and thermal interactions in missile systems so engineers can better understand how a product will operate at hypersonic speeds. But any industry can use AI and ML to help teams make better and smarter design choices and, ultimately, better products and experiences for customers and end users.
At companies like Raytheon, where security and safety are a top concern, the data must be organized and firewalled to ensure that only those with proper access can view classified information. But once implemented, a modern data architecture has also helped Raytheon’s teams navigate a global supply chain that continues to face challenges caused by the pandemic and ongoing political strife.