Techniques Boost Friction Stir Welding Efficiency

A Combination of Tools and Analyses Improves Weld Strength and Reduces Defects

Mar. 17, 2026 at 1:08am

Got story updates? Submit your updates here. ›

NASA's NESC team developed several innovative techniques, including machine learning, statistical modeling, and physics-based simulations, to improve the self-reacting friction stir welding (SRFSW) process. The assessment helped identify root causes for poor tensile strength and low topography anomalies (LTA) in welds, leading to process adjustments that eliminated these issues in production.

Why it matters

Friction stir welding is a critical technique for joining high-performance metal alloys that are traditionally difficult to weld, enabling the use of advanced materials in aerospace applications like the Space Launch System (SLS) rocket. Improving the reliability and efficiency of this welding process is crucial for reducing manufacturing costs and ensuring the structural integrity of critical components.

The details

The NESC team used a combination of machine learning, statistical modeling, and physics-based simulations to tackle the issues with the SRFSW process. They developed a deep learning model to automatically detect and segment LTA in weld images, eliminating the need for manual identification. An integrated data-ingestion framework was created to manage the diverse data types produced during welding, and a web-based visualization tool allowed for faster hypothesis testing. The team also implemented a space-filling design of experiments to efficiently explore the full parameter space, and a computational model of the SRFSW process provided insights into the weld conditions and microstructure evolution.

  • The NESC assessment was conducted in 2026.

The players

NESC

NASA's Engineering and Safety Center, which conducted the assessment to improve the self-reacting friction stir welding process.

NASA's Michoud Vertical Assembly Center

The location of NASA's Friction Stir Welding lab, where the SRFSW technique is used to join major components of the SLS rocket.

Got photos? Submit your photos here. ›

The takeaway

The techniques developed by the NESC team, including machine learning, data integration, and physics-based simulations, have helped improve the reliability and efficiency of the friction stir welding process, enabling the use of advanced materials in critical aerospace applications like the SLS rocket. This demonstrates the value of innovative, data-driven approaches to solving complex manufacturing challenges.