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Results for “DO 178-C”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Dimensional Stability of Complex Shapes Manufactured by the VARTM Process

The vacuum assisted resin transfer molding (VARTM) process is a cost effective, innovative method that is being considered for manufacture of large aircraft-quality components where high mechanical properties and dimensional tolerance are essential. In the present work, carbon fiber SAERTEX fabric/SI-ZG-5A epoxy resin C-shaped laminates were manufactured by VARTM using different cure cycles followed by the same post-cure cycle. The final part thickness was uniform except at the corner were thinning was observed. The cure cycle selected is shown to significantly affect the part spring-in and a long cycle at 66 C followed by a 178 C post-cure produced a part with negligible spring-in.

Hubert, Pascal↗
NASA NTRSDate not supplied

Runway Sign Classifier: A DAL C Certifiable Machine Learning System

In recent years, the remarkable progress of Machine Learning (ML) technologies within the domain of Artificial Intelligence (AI) systems has presented unprecedented opportunities for the aviation industry, paving the way for further advancements in automation, including the potential for single pilot or fully autonomous operation of large commercial airplanes. However, ML technology faces major incompatibilities with existing airborne certification standards, such as ML model traceability and explainability issues or the inadequacy of traditional coverage metrics. Certification of ML-based airborne systems using current standards is problematic due to these challenges. This paper presents a case study of an airborne system utilizing a Deep Neural Network (DNN) for airport sign detection and classification. Building upon our previous work, which demonstrates compliance with Design Assurance Level (DAL) ”D”, we upgrade the system to meet the more stringent requirements of Design Assurance Level ”C”. To achieve DAL C, we employ an established architectural mitigation technique involving two redundant and dissimilar Deep Neural Networks. The application of novel ML-specific data management techniques further enhances this approach. This work is intended to illustrate how the certification challenges of ML-based systems can be addressed for medium criticality airborne applications.

Flight Software↗