Machine learning holds significant promise for driving new efficiencies and optimizations in aerospace manufacturing. However, many potential application areas for machine learning in manufacturing are hampered by the dual difficulties of small labeled training dataset size and class imbalance. Leveraging generative AI for synthetic data creation can efficiently address these challenges and lower the barrier to entry for deploying successful machine learning solutions in the manufacturing domain. In this presentation we will detail the development and deployment of machine learning capabilities for foreign object debris (FOD) detection, environmental alerting using IoT sensor data, and quality control in additive manufacturing.
Data ScientistNorthrop Grumman