Chin Kian Liew

Pattern recognition of guided waves for damage evaluation in bars

Guided waves damage identification in bars with neural networks acquires training data from simulation as a cost-effective measure. These neural networks applied with a novel test inputs dependent iterative training scheme are capable of quantifying damages accurately from experimental inputs. The reliability of the predictions depends on the quality of the measured signals, which can be increased by considering more than one signal obtained from different sensor locations or by changing the properties of the interrogation pulse. A parallel network system to process the inputs from these signals collaboratively is described. The core of the system is a data fusion process that associates overlapping intermediate test results while isolating outliers to narrow the training range for improved generalization in the iterative test inputs dependent training scheme. This robust system of signal processing has achieved accurate average damage quantitative results with errors below 4% and 13% the original size of the training parameter space for damage location and depth, respectively, of artificial laminar defects in bars.

Single-Plant Biocomposite from Ricinus Communis: Preparation, Properties and Environmental Performance

A single-plant biobased composite material was prepared from fibre and matrix constituents produced from the castor plant, ricinus communis. It is shown that the mechanical properties of the castor plant fibres are comparable to those of other bast fibres and that the stiffness and strength characteristics of the castor fibre/polyamide 11 biocomposite compare well with those of other natural fibre composites. By using a biobased thermoplastic matrix material the reliance on non-renewable feedstock sources is reduced and end-of-lifetime recyclability is improved. The analysis of the environmental performance of the new castor plant composite suggests that the biobased material has great potential as a sustainable alternative replacing glass fibre-reinforced plastics.

Inspections of Helicopter Composite Airframe Structures using Conventional and Emerging Nondestructive Testing Methods

This paper presents nondestructive testing (NDT) results and analysis from the inspection of composite specimens representing typical helicopter parts. The specimens include monolithic laminates produced from carbon fiber reinforced plastic (CFRP), Nomex honeycomb core sandwich panels with CFRP skins, and CFRP frame-skin joint panels. External protection layers comprising copper mesh and fiberglass were also included in the specimens. These panels were fabricated with a wide range of defects to simulate helicopter in-service damage including delamination and skin-core disbond along with barely visible impact damage. The study aims to assess a number of conventional and emerging NDT techniques suitable for rapid in situ and off-site inspection of helicopter composite structures. The techniques considered are flash and sonic thermography, radiography, and different ultrasonic inspection modes including pulse-echo, through-transmission, and phased array. These techniques are compared on their ability to detect and characterize the fabricated defects.

Optimization of Neural Network Pattern Recognition Systems for Guided Waves Damage Identification in Beams

Neural network pattern recognition is an advanced regression technique that can be applied to identify guided wave response signals for quantifying damages in structures. This paper describes a procedure to optimize the design of a multi‐layer perceptron backpropagation neural network with signals preprocessed by the wavelet transform. The performance can be further improved using a weight‐range selection technique in a series network since there is increased sensitivity of the neural network to experimental damage patterns if the training range is reduced. Damage identification in beams with longitudinal guided waves is used in this study.

A Wavelet-Based Damage Detection Approach for Acousto-Ultrasonic In Situ Monitoring Systems

In this research, an advanced signal processing technique using wavelet analysis has been developed for a guided wave structural health monitoring system. The approach was applied for the detection of delamination in carbon fibre reinforced composites. A monolithic piezoceramic actuator was attached to a laminate plate for wave generation while laser vibrometry was used to facilitate the measurements of the wave response in a sensor network. This database of wave response was then processed using the continuous wavelet transform to obtain the positional frequency content. Transforms between damaged and undamaged states were compared to ascertain the presence of defects by evaluating the total energy of the time-frequency density function. Results show high damage detection indices depending on the location of the sensor and normalisation factor applied while there are positive indications that this methodology can be extended for damage characterisation.

Non-destructive evaluation (NDE) of aerospace composites: structural health monitoring of aerospace structures using guided wave ultrasonics

Monitoring safety-critical structures over their lifetime in order to improve reliability and availability and reduce life-cycle costs is of vital importance in many technical fields. Aerospace, with zero catastrophic failure tolerance, is the leader in the development of structural health monitoring (SHM) systems and considering the constantly increasing use of composite materials in aerospace applications, the development of SHM systems for aerospace composite materials and structures is of particular interest. This chapter outlines the present progress of health monitoring of aerospace composite structures by compiling the technologies used and summarising significant contributions made. First the three major transducer systems used are briefly reviewed. This is followed by a detailed discussion of the current status in four main areas of guided waves ultrasonic SHM systems development, namely transducer integration, simulation techniques, transducer network optimisation and signal processing. The results show that SHM will be one of the key technologies to ensure the structural integrity of ageing and future composite aircraft structures. At the same time it is recognised that a step-by-step implementation strategy is required to ensure direct technological and economical benefits, in which SHM systems with increasing complexities are taking over more and more responsibility.

Damage evaluation in smart materials using time-frequency analysis

An approach to identify damages in materials particularly aerospace composites has been developed based on application of wavelet analysis on guided wave transient signals. The wave response was convoluted against a suitable wavelet to present information in the time-frequency domain for baseline comparison of signals. By evaluating the time-frequency conditional moment, sensitive indices were computed to identify the presence of damage. Normalisation of these indices was found to be an important procedure to reduce measurement discrepancies from baseline comparison. Extending this further was the correlation of these parameters to pulse propagation time from actuator to sensor positions to generate a tomographic representation of the damage in the scanned material. These damage evaluation processes were investigated in an ultrasonic health monitoring system inspecting carbon fibre reinforced composite panels with simulated delamination. The imaging results displayed positive damage characterisation capabilities for implementation within the methodology of smart or intelligent materials.

Application of Wavelet Parameters for Impact Damage Detection in Plates

This study proposes a new nondestructive evaluation methodology named laser lock-in thermography (LLT) for fatigue crack detection. LLT utilizes a high power continuous wave (CW) laser as a heat generation source for lock-in thermography instead of commonly used flash and halogen lamps. The advantages of the proposed LLT method are that (1) the laser heat source can be positioned at an extended distance from a target structure thank to the directionality and low energy loss of the laser source, (2) thermal image degradation due to surrounding temperature disturbances can be minimized because of high temperature gradient generated by the laser source and (3) a large target surface can be inspected using a scanning laser heat source. The developed LLT system is composed of a modulated high power CW laser, galvanometer and infrared camera. Then, a holder exponent-based data processing algorithm is proposed for intuitive damage evaluation. The developed LLT is employed to detect a micro fatigue crack in a metal plate. The test result confirms that 5 μm (or smaller) fatigue crack in a dog-bone shape aluminum plate with a dimension of 400 x 140 x 3 mm3 can be detected.