Simulated Inspection Mechanics for Artificial Intelligence Defect Detection (SIM-AID)

In this study; we propose to use X-Ray computed tomography simulation to generate artificial intelligence (AI) defect detection training data for additively manufactured metals. This topic leverages the team’s extensive experience in the areas of X-Ray inspection; X-Ray simulation; AI; and image segmentation. We intend to extend our X-Ray simulation methods to generate AI training data over a representative defect parameter space; and use AI to detect defects in additive manufactured metal components.

As NASA aims to expand our use of additive manufacturing; our nondestructive evaluation capabilities need to similarly improve to avoid bottlenecking such advanced materials and capabilities. The use of modern artificial intelligence (AI) defect detection helps to bolster our ability to inspect such advanced material systems. This project explores methods to use physics-based simulation to expand training data; allowing us to deploy AI defect detection even on parts with small production runs or with scarcity in available defect data.

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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