Tag Archive for: machine learning

The collaboration between Machina Labs and various entities in the space sector underscores the growing role of robotics and artificial intelligence (AI) in advancing manufacturing processes. Here are key points regarding Machina Labs’ involvement:

  1. Established in 2019: Machina Labs, a Los Angeles-based startup, was founded in 2019, and it has rapidly expanded its presence in the space sector. The company focuses on leveraging robotics and AI to enhance manufacturing processes, particularly in the context of space-related applications.
  2. Collaboration with NASA and AFRL: The startup has collaborated with prominent organizations such as NASA and the United States Air Force Research Laboratory (AFRL). The work with AFRL specifically targeted the development of robotic technology for manufacturing metal tooling used in composite structures. This indicates a commitment to advancing manufacturing capabilities through innovative technologies.
  3. Machine Learning for In-Space Manufacturing: In collaboration with NASA, Machina Labs applied machine learning-based software for in-space manufacturing, particularly with autonomous articulated robots. This aligns with the broader trend of incorporating AI solutions into space exploration and manufacturing processes.
  4. Partnership with Satellite Manufacturers: Machina Labs has extended its services to satellite manufacturers, assisting them in rapidly iterating designs. The ability to quickly iterate and test designs is crucial for optimizing satellite components and systems. The company’s involvement in this area highlights the significance of advanced manufacturing techniques in satellite development.
  5. Innovative Manufacturing Process – Roboforming: Machina Labs introduced a manufacturing process called Roboforming. This process is specifically mentioned for its application in producing toroidal propellant tanks. Traditionally, manufacturing such tanks has been challenging and time-consuming. The Roboforming process aims to reduce costs and accelerate the manufacturing of these specialized tanks.
  6. Expertise of Machina Labs Leadership: Edward Mehr, the CEO and co-founder of Machina Labs, brings a background as a former Relativity program manager and SpaceX software engineer. This experience in the aerospace and space industries likely contributes to the company’s ability to address challenges in space-related manufacturing.

In summary, Machina Labs’ work reflects the ongoing integration of advanced technologies, including robotics and AI, in space-related manufacturing processes. As the space industry continues to evolve, such innovations play a vital role in enhancing efficiency, reducing costs, and pushing the boundaries of what is achievable in space exploration and satellite development.

Machina Labs’ involvement in working with hypersonic vehicles and the utilization of artificial intelligence (AI) and machine learning (ML) in their processes highlights the cutting-edge technologies being applied in aerospace manufacturing. Here are key points related to their work in these areas:

  1. Hypersonic Vehicles and Material Toughness: Machina Labs is engaged in developing manufacturing processes for hypersonic vehicles, which are known for their high-speed flight capabilities. The company works with materials, including titanium and Inconel, that possess the necessary toughness to withstand the extreme heat generated during reentry. This indicates a focus on addressing the unique material challenges associated with hypersonic flight.
  2. AI and ML in Metal Deformation: Machina Labs employs machine learning, utilizing Nvidia chips, to replicate the work of craftsmen involved in incrementally deforming metals or composites to create specific shapes. The goal is to replicate the decision-making process that occurs in the mind of a skilled craftsman. This involves building empirical models of how materials deform during shaping, allowing the AI systems to determine the appropriate processes for different geometries.
  3. Empirical Models for Deformation: Machina Labs has developed empirical models that capture the deformation characteristics of materials throughout the shaping process. These models serve as a foundation for the AI and ML algorithms to understand how materials respond to various manufacturing processes. This data-driven approach enables a more precise and efficient manufacturing process.
  4. Determining Process Parameters: The company’s engineers leverage the empirical models to determine the right set of process parameters for each geometry. This involves understanding the specific requirements for shaping different parts and guiding robots to execute the necessary actions. The combination of AI, ML, and robotics facilitates a level of precision that is crucial in aerospace manufacturing.

Machina Labs’ work reflects the integration of advanced technologies not only in the aerospace industry but specifically in addressing challenges related to hypersonic vehicles. The use of AI and ML for replicating craftsmanship and determining optimal manufacturing processes showcases the potential of these technologies in revolutionizing traditional manufacturing approaches. As the aerospace sector continues to advance, such innovations contribute to increased efficiency and capabilities in the production of complex aerospace components.

According to research, combining satellite technology with machine learning may allow scientists to better track and prepare for climate-induced natural hazards.

During the past century, many natural phenomena like hurricanes, snowstorms, floods and wildfires have grown in intensity and frequency.

While humans can’t prevent these disasters from occurring, the rapidly increasing number of satellites that orbit the Earth from space offers a greater opportunity to monitor their evolution, said C.K Shum, co-author of the study and a professor at the Byrd Polar Research Center and in earth sciences at The Ohio State University. He said that potentially allowing people in the area to make informed decisions could improve the effectiveness of local disaster response and management.

“Predicting the future is a pretty difficult task, but by using remote sensing and machine learning, our research aims to help create a system that will be able to monitor these climate-induced hazards in a manner that enables a timely and informed disaster response,” said Shum.

Shum’s research uses geodesy—the science of measuring the planet’s size, shape and orientation in space—to study phenomena related to global climate change.

Using geodetic data gathered from various space agency satellites, researchers conducted several case studies to test whether a mix of remote sensing and deep machine learning analytics could accurately monitor abrupt weather episodes, including floods, droughts and storm surges in some areas of the world.

In one experiment, the team used these methods to determine if radar signals from Earth’s Global Navigation Satellite System (GNSS), which were reflected over the ocean and received by GNSS receivers located at towns offshore in the Gulf of Mexico, could be used to track hurricane evolution by measuring rising sea levels after landfall. Between 2020 and 2021, the team studied how seven storms, such as Hurricane Hana and Hurricane Delta, affected coastal sea levels before they made landfall in the Gulf of Mexico. By monitoring these complex changes, they found a positive correlation between higher sea levels and how intense the storm surges were.

The data they used was collected by NASA and the German Aerospace Center’s Gravity Recovery And Climate Experiment (GRACE) mission, and its successor, GRACE Follow-On. Both satellites have been used to monitor changes in Earth’s mass over the past two decades, but so far, have only been able to view the planet from a little more than 400 miles up. But using deep machine learning analytics, Shum’s team was able to reduce this resolution to about 15 miles, effectively improving society’s ability to monitor natural hazards.

“Taking advantage of deep machine learning means having to condition the algorithm to continuously learn from various data inputs to achieve the goal you want to accomplish,” Shum said. In this instance, satellites allowed researchers to quantify the path and evolution of two Category 4 Atlantic hurricane-induced storm surges during their landfalls over Texas and Louisiana, Hurricane Harvey in August 2017 and Hurricane Laura in August 2020, respectively.

Accurate measurements of these natural hazards could one day help improve hurricane forecasting, said Shum. But in the short term, Shum would like to see countries and organizations make their satellite data more readily available to scientists, as projects that rely on deep machine learning often need large amounts of wide-ranging data to help make accurate forecasts.

“Many of these novel satellite techniques require time and effort to process massive amounts of accurate data,” said Shum. “If researchers have access to more resources, we’ll be able to potentially develop technologies to better prepare people to adapt, as well as allow disaster management agencies to improve their response to intense and frequent climate-induced natural hazards.”