Ph.D. Candidate Embry-Riddle Aeronautical University
Additive manufacturing (AM) enables fabrication of complex, high-strength geometries for rapidly developing new products that are not practical through conventional methods. The originality of this work lies in printing internal channels that allow fiber optic sensors to be embedded directly inside load bearing components. This approach enables measurements of deformation and temperature in regions that are otherwise inaccessible, without affecting external geometry. The method is demonstrated through the development and testing of an additively manufactured cryogenic fuel tank and a representative Perseverance rover wheel. The research maintains full impartiality because it is conducted within a nonprofit academic environment and does not promote any commercial sensing technology, even though the technique has attracted interest from industry.
The objectives of the presentation are to provide engineering professionals with practical insight into the integration of embedded sensing systems in AM components and to demonstrate the technical depth required to meet NASA-STD-6030 standards for space rated structures. Detailed testing of the cryogenic tank illustrates the performance of the embedded sensors, and their results are evaluated alongside established ground measurement methods such as digital image correlation and thermography. These comparisons supply evidence and credibility for the sensing approach and highlight its potential value for verification and validation of complex AM hardware. The presentation concludes with lessons learned and guidance on how embedded sensing can support digital twin development and future aerospace and industrial AM applications.
Learning Objectives:
Understand what is needed to certify additively manufactured structures for use in space following NASA-STD-6030.
Understand how fiber optic sensing systems can be embedded inside of structures to capture high fidelity deformation and temperature data.
Become more aware of how sensing can be used to improve lifecycle predictions of structures.