The vibro-acoustic modelling and analysis of damage mechanisms in paper
This thesis investigates the use of the acoustic emission (AE) monitoring technique for use in identifying the damage mechanisms present in paper associated with its production process. The microscopic structure of paper consists of a random mesh of paper fibres connected by hydrogen bonds. This implies the existence of the two damage mechanisms of interest, the failure of a fibre/fibre bond and the failure of a fibre. The majority of this work focuses on the development of a novel hybrid mathematical model which couples the mechanics of the mass/spring model to the acoustic wave propagation model for use in generating the acoustic signal emitted by complex structures of paper fibres under strain. A discussion of the coupling method is presented and the model is then analysed using a simple plucked fibre as a test case with a comparison between the numerical and experimental results. The hybrid mathematical model is then used to simulate small fibre networks aimed at providing information on the acoustic response of each damage mechanism. To do this the mass/spring model must successfully simulate the response of the fibre structure when undergoing a fibre/fibre bond failure or a fibre failure. This can be achieved by dynamically manipulating the mass and spring elements of the fibre structure. The simulated AEs from the two damage mechanisms are then analysed using a Continuous Wavelet Transform (CWT) to provide a two dimensional time/frequency representation of the signal. From the CWT certain features of the AEs can be attributed to each damage mechanism and as such a criteria for the time and frequency properties of each damage mechanism can be formulated. This criterion provides the basis for identifying the damage mechanisms present in the experimental data. The final contribution of this thesis is the investigation of training an intelligent classifier which can dynamically identify the AEs from the two damage mechanisms. This is achieved by converting the time and frequency criteria for each damage mechanisms into a set of features for the training of a Self-Organising Map (SOM). The significant step in this analysis is the method for the extraction of the features from the CWT of the AE. This work successfully combines four different scientific areas, paper physics, acoustic emission technology, data analysis and computational modelling to provide an insight into the micro-mechanics of paper. The most significant contribution of this work is the development of the hybrid model which has the ability to generate the acoustic response of a paper fibre structure undergoing two different damage processes. This alone has provided a significant insight into the micro-mechanics of paper to allow for the identification of the two damage mechanisms when the AEs are analysed with the CWT. Other contributions include the method used for the extraction of relevant features from the CWT to enable the training of a SOM for identifying the type of damage mechanism the AE originated from.