Fiber Reinforced Composites as Self-Diagnosis Materials for Memorizing Damage Histories

Yoshiki Okuhara, Byung Koog Jang, Hideaki Matsubara, Minora Sugita

研究成果: ジャーナルへの寄稿Conference article

7 引用 (Scopus)


Electrically conductive fiber-reinforced composites have been designed in order to develop self-diagnosis materials with the ability to memorize damage histories. Irreversible resistance changes dependent on the strain histories of the composites were utilized to achieve this ability. Conductive fiber-reinforced plastics for memorizing maximum strain were prepared by adding carbon fibers or particles into the composites. Pre- tensile stresses in composites containing carbon fibers were found to effectively enhance their residual resistance and to significantly improve the limit of smallest detectable strains. The residual resistances of composites containing carbon particles connected by a percolation structure were found to depend strongly on the volume fractions of carbon particles; composites with high volume fractions of carbon displayed remarkable residual resistance without application of a pre-tensile stress. In order to memorize cumulative damage, composites consisting of a brittle titanium nitride ceramic wire laminated with glass fiber reinforced plastics were prepared. These composites were found to exhibit remarkable residual resistances that increased in proportion to the logarithm of the number of tensile cycles. These results suggest that a simple and low cost monitoring technique without real-time measurement system will be available in wide range of applications using these composites.

ジャーナルProceedings of SPIE - The International Society for Optical Engineering
出版物ステータス出版済み - 11 26 2003
イベントSmart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures - San Diego, CA, 米国
継続期間: 3 3 20033 6 2003


All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering