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Multiscale Models of Multifunctional Composites for On-Board Damage Detection and Failure Prevention

Journal article published in 2 by W. A. Curtin
This paper was not found in any repository; the policy of its publisher is unknown or unclear.
This paper was not found in any repository; the policy of its publisher is unknown or unclear.

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Abstract

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Approved for Public Release, Distribution is unlimited Analytic and numerical models were developed to predict the dependence of electrical resistance on internal fiber damage in carbon-fiber-reinforced polymer (CFRP) composites, so as to make electrical resistance measurement a tool for damage detection and prognosis in CFRP components. The models show that electrical resistance is more sensitive to damage than elastic modulus, that resistance change can be used to detect inadvertent overloads, that the remaining fatigue life after overload cycles can be accurately determined, and that the statistical distribution of electrical resistance changes is narrow enough to permit accurate assessment of the damage state of the material. Macroscopic models of components with damage in the form of holes or delaminations were also developed and their predictions correlated with experimental measurements.