journal article·2001-12

In situ monitoring in real time of fatigue-induced damage growth in composite materials by acoustography

A.S Chen, D.P Almond, B Harris

Composites Science and Technology· Volume 61· Issue 16
Publisher page

Abstract

Abstract There are growing concerns about the effects of accidental impact damage on the structural integrity of aerospace composites and about the possible growth of the damage due to in-service fatigue. There has been some success in the use of established methods (ultrasonic C-scan, thermography, X-rays) to monitor damage development during fatigue experiments by interrupting a test and removing the specimen for damage inspection but this stop-and-restart test procedure is far from satisfactory. Real-time damage monitoring in composite materials during fatigue has now become possible by the emergence of a new ultrasonic imaging technology, acoustography. The successful integration of acoustography and a servo-hydraulic fatigue test machine has resulted in a new measurement system which can be used for the in situ monitoring in real time of damage growth in composite specimens during long-term fatigue tests. Results are presented which show damage-area growth during fatigue cycling under high compressive loads. After an initial small enlargement (stage 1), damage grows at a constant rate (stage 2) until the third stage is reached when there is further growth at an increasing rate to final failure. However, a ‘fatigue limit’ has also been observed. At stresses below this fatigue limit, a zero damage-growth regime has been found in studies of >10 6 fatigue cycles. The results obtained have important implications for the understanding of the effects of damage on fatigue life and for the design of ‘safe’ damage-tolerant structures.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Ultrasonics and Acoustic Wave Propagation · Thermography and Photoacoustic Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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