Ono

ono_100.jpg Professor Kanji Ono
Tel. (310) 825-5233
FAX (310) 206-7353
Email:

Professor Emeritus; B.Engr. Tokyo Institute of Technology; Ph.D. Northwestern University; Postdoctoral research, Northwestern University; Visiting Professor, International Christian University; Director, Tokyo Study Center, University of California Education Abroad Program; Henry M. Howe Medal, American Society for Metals; Achievement Award, American Society for Nondestructive Testing; Achievement Award and Gold Medal Award, Acoustic Emission Working Group; Editor, Journal of Acoustic Emission.

Research Interests

Our research is about the mechanical behavior and nondestructive evaluation (NDE) of structural materials

Of late, we concentrate on composite materials and aluminum alloys, using acoustic emission and related ultrasonic methods. In particular, we examine techniques to predict incipient fracture with advanced acoustic emission analysis, relying on digital signal acquisition and pattern recognition processing.

Acoustic emission (AE) has demonstrated capabilities for monitoring structural integrity and for dynamically characterizing materials behavior. Fatigue fracture in aluminum alloys and micro-fracture processes in fiber reinforced composites generate numerous acoustic emission (AE) signals that can be detected. With proper analysis, these AE signals can dynamically identify failure processes. However, conventional AE methods with the use of event counts, event energy, signal amplitude, duration and rise time, etc. have severe limitation in the characterization of AE sources. For fatigue detection and for failure mechanism study in composite materials, advanced characterization methods are needed. By using sensor output waveforms, identifying unknown signals and evaluating their significance, and correlating identified signals to the failure modes, the advanced methods permit quantitative interpretations based on signal feature analysis and identify the nature of emission sources. The techniques of pattern recognition are used to classify unknown signals into groups, which are related to specific signal characteristics.

Office Location

  • 2121-H Engineering V

Mailing Address

  • UCLA, HSSEAS School of Engineering & Applied Sciences
    Department of Materials Science and Engineering
    410 Westwood Plaza
    3111 Engineering V
    Los Angeles, CA 90095-1595