Acoustic emission analysis of the effect of a 2D wedge shaped blade on the compact bone cutting process

Surgeons may use a number of cutting instruments such as osteotomes and chisels to cut bone during an operative procedure. The initial loading of cortical bone during the cutting process results in the formation of microcracks in the vicinity of the cutting zone with main crack propagation to failure occuring with continued loading. When a material cracks, energy is emitted in the form of Acoustic Emission (AE) signals that spread in all directions, therefore, AE transducers can be used to monitor the occurrence and development of microcracking and crack propagation in cortical bone. In this research, number of AE signals (hits) and related parameters including amplitude, duration and absolute energy (abs-energy) were recorded during the indentation cutting process by a wedge blade on cortical bone specimens. The cutting force was also measured to correlate between load-displacement curves and the output from the AE sensor. The results from experiments show AE signals increase substantially during the loading just prior to fracture between 90% and 100% of maximum fracture load. Furthermore, an amplitude threshold value of 64dB (with approximate abs-energy of 1500 aJ) was established to saparate AE signals associated with microcracking (41 – 64dB) from fracture related signals (65 – 98dB). The results also demonstrated that the complete fracture event which had the highest duration value can be distinguished from other growing macrocracks which did not lead to catastrophic fracture. It was observed that the main crack initiation may be detected by capturing a high amplitude signal at a mean load value of 87% of maximum load and unsteady crack propagation may occur just prior to final fracture event at a mean load value of 96% of maximum load. The author concludes that the AE method is useful in understanding the crack initiation and fracture during the indentation cutting process.

Instrumentation and software for the VERITAS Project and the detection of TeV Gamma-Ray Emission from the BL Lacertae Object 1ES1959+650

Transmission of Cherenkov light through the atmosphere is strongly influenced by the optical clarity of the atmosphere and the prevailing weather conditions. The performance of telescopes measuring this light is therefore dependent on atmospheric effects. This thesis presents software and hardware developed to implement a prototype sky monitoring system for use on the proposed next-generation gamma-ray telescope array, VERITAS. The system, consisting of a CCD camera and a far-infrared pyrometer, was successfully installed and tested on the ten metre atmospheric Cherenkov imaging telescope operated by the VERITAS Collaboration at the F.L. Whipple Observatory in Arizona. The thesis also presents the results of observations of the BL Lacertae object, 1ES1959+650, made with the Whipple ten metre telescope. The observations provide evidence for TeV gamma-ray emission from the BL Lacertae object, 1ES1959+650, at a level of more than 15 standard deviations above background. This represents the first unequivocal detection of this object at TeV energies, making it only the third extragalactic source seen at such levels of significance in this energy range. The flux variability of the source on a number of timescales is also investigated.

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.