A Combination of Techniques Leads to Improved Friction Stir Welding
Download PDF: A Combination of Techniques Leads to Improved Friction Stir Welding The NESC developed several innovative tools and techniques during an assessment to find the root cause of poor tensile strength and low topography anomalies (LTA) in welds formed using a solid-state welding process called self-reacting friction stir welding (SRFSW). Using a combination of machine learning, statistical modeling, and physics-based simulations, the assessment team helped improve the weld process and solve both issues, lifting constraints that had been placed on flight hardware. Developing Techniques for LTA Detection Determining the root cause of poor tensile strength welds and LTA observed on the weld fracture surfaces involved several techniques: Deep Learning for LTA Detection: The NESC team developed a machine-learning model to detect and segment LTA in weld images. The model was trained on images annotated by metallurgy experts, with...
