Thesis (Ph. D.)--University of Texas at Austin, 2003. / Supervisors: Richard A. Schapery and K. Ravi-Chandar. Vita. Includes bibliographical references. Also available from UMI.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 333-342). Available also in a digital version from Dissertation Abstracts.

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Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features.

Sudden rock failure in the form of rockbursting has long been a problem in underground mines. The basic mechanism of this phenomenon is still unresolved. This thesis describes the research work on this problem conducted by the doctoral candidate Daihua Zou in the Department of Mining and Mineral Process Engineering at The University of British Columbia, under the supervision of Professor Hamish D.S. Miller. This research project was undertaken in order to investigate the process of violent rock failure and was achieved by examining various aspects of the rock failure mechanism. The assumption that acoustic emission can be used as a reliable means of predicting rock failure was investigated, as well as the possibility that violent rock failure could occur in any mine rock. As part of the research, a rock failure mechanism was postulated. A process analogous to shearing is postulated to be important at the post-failure stage. The stick-slip phenomenon has been analyzed using a numerical model under a variety of conditions. The conditions which could give rise to possible violent rock failure were determined. At the same time, acoustic emissions were tested from rock specimens under different loading conditions. The experimental results obtained show a correlation with field measurements made in a mine. In order to verify the testing results from limited experiments, a numerical acoustic model was developed, which is unique in that it is entirely based on the stick-slip process not on any acoustic theory. This model allows rock tests and their associated acoustic emission to be realistically simulated. With this model, acoustic emissions were simulated under various loading conditions for different kinds of rocks. The case of a hard or a soft intercalation was also modelled. / Applied Science, Faculty of / Mining Engineering, Keevil Institute of / Graduate

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

Bubble and cavitation effects phenomena can be encountered in two-phase gas-liquid systems in industry. In certain industries, particularly high-risk systems such as a nuclear reactor/plant, the detection of bubble dynamics, and the monitoring and measurement of their characteristics are necessary in controlling temperature. While in the petro-chemical engineering industry, such as oil transportation pipelines, the detection and monitoring of bubbles/cavitation phenomena are necessary to minimise surface erosion in fluid carrying components or downstream facilities. The high sensitivity of Acoustic Emission (AE) technology is feasible for the detection and monitoring of bubble phenomena in a two phase gas-liquid system and is practical for application within the industry. Underwater measurement of bubble oscillations has been widely studied using hydrophones and employing acoustic techniques in the audible range. However, the application of Acoustic Emission (AE) technology to monitor bubble size has hitherto not been attempted. This thesis presents an experimental investigation aimed at exploring AEs from gas bubble formation, motion and destruction. AE in this particular investigation covers the frequency range of between 100 kHz to 1000 kHz. The AE waveform analysis showed that the AE parameter from single bubble inception and burst events, i.e. AE amplitude, AE duration and AE energy, increased with the increase of bubble size and liquid viscosity. This finding significantly extends the potential use of AE technology for detecting the presence of bubbles in two-phase flow. It is concluded that bubble activity can be detected and monitored by AE technology both intrusively and non-intrusively. Furthermore, the bubble size can be determined by measurement of the AE and this forms the significant contribution of this thesis.