Data Fusion for Self-Sensing Additively Manufactured Polymer Matrix Composite State Awareness (S2AM-PMCSA)
Abstract
Self-sensing materials have long been advertised as a way of providing integrated full-field measurement information for material state awareness. But this potential has yet to be reached because self-sensing materials by themselves only provide qualitative information. Actually determining the underlying quantitative state of the material (e.g., strains, stresses, damages, etc.) requires solving a complex and ill-posed inverse problem. This is referred to as the self-sensing inverse problem (SSIP). Because many materials exhibit self-sensing properties in many highly US Air Force-relevant applications—embedded diagnostics, full-field system awareness, integrated nondestructive evaluation (NDE), proprioception and dynamic shape feedback in advanced morphing-structure concepts, intelligent manufacturing of advanced materials, and, among others, extreme materials testing—there exists considerable motivation to efficiently and accurately solve this inverse problem. Therefore, this research hypothesizes that the SSIP can be rendered as well-posed and uniquely solvable through the application of sensor and data fusion techniques. The overall goal of this work is to make the SSIP in materials well-posed by stabilizing it over continuous parameter changes such that a unique solution exists. These concepts will be validated on direct ink write (DIW)-produced carbon fiber-carbon nanotube (CNT)-epoxy additively manufactured (AM) polymer matrix composites (PMCs). This material system is targeted because it is of keen interest to the Air Force for attritable aircraft and because of the offeror’s extensive experience in self-sensing PMCs. This research will supplement collaborative work between the Air Force Research Lab (AFRL) and the offeror on integrated sensing in attritable aircraft. If successful, this research will allow next-generation attritables to be fully mechanically self-aware of in-operation stresses, strains, damages, and shape changes. This information can be used to guide mission profiles while simultaneously reducing inspection and labor costs of these vehicles.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 29, 2024
- Source ID
- FA95502310317
Entities
People
- Tyler N. Tallman
Organizations
- Air Force Office of Scientific Research
- Purdue University
- United States Air Force