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Title: Methodology platform for prediction of damage events for self-sensing aerospace panels subjected to real loading conditions
Author: Marks, Ryan
ISNI:       0000 0004 5916 099X
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2016
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With the growing size of aircraft fleets and the complexity of aircraft structures it has been proposed that there are many cost and operational benefits of installing a structural health monitoring system to monitor the aircraft’s structure throughout its in-service life. A method of achieving this is through monitoring the acoustic emission emitted during a damage event. One of the limiting factors to this however is having sufficient confidence in the placement of the sensors to ensure coverage while limiting the mass associated with the system. A series of five studies were conducted which use both experimental and numerical approaches to investigate Lamb wave propagation and its interaction with damage in both metallic and composite materials. These studies have used some of this data and through the use of genetic algorithms sought to optimise the placement of sensors with the objective of achieving a high probability of damage detection. The use of 3D scanning laser vibrometry has been harnessed along with the use of numerical reasoning using the local interaction simulation approach. This has enabled studies to be conducted which consider both the in-plane and out-of-plane components of the Lamb waves which is an important consideration when selected the appropriate sensing methods. In addition, a novel method of training sensor networks for AE location using the delta-t technique is also presented. The results of these studies has led to the development of two separate methodologies; one for the placement of sensors in an acousto-ultrasonic system for the detection of adhesive disbonds and one for the placement of AE sensors to maximise the coverage of the sensor network on a structure with complex geometry. These methodologies have many advantages, particular in their prompt convergence which makes progress towards enabling a concurrent sensor network-structure development.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TJ Mechanical engineering and machinery