Prediction and Detection of Failures in Laminated Composite Materials Using Neural Networks - A Review
Source abstract
Abstract
Reliable failure detection and prediction in laminated composite materials are critical for the utilization of these materials. Many quantitative techniques have been successfully researched and implemented for failure detection and prediction. Most of them are based on non-destructive methods for data collection and are quantified by using neural networks. In this paper a review of the subject is presented. The non-destructive techniques considered are limited to natural frequencies, electric conductivity, and lamb waves. The paper gives a brief introduction to each technique followed by summaries and evaluations for some relevant studies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Materials for Construction · Structural Health Monitoring Techniques · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Intelligent Systems and Robotics, Institute of Advanced Technology Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Department of Mechanical and Manufacturing Engineering Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Department of Aerospace Engineering Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Department of Communication Technology and Networks, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
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