Thesis (M.S.)--Mississippi State University. Department of Agricultural and Biological Engineering. / Title from title screen. Includes bibliographical references.

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Thesis--University of Washington. / Vita. Description based on print version record. Includes bibliographical references (leaves [147]-156).

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.

Not available / text

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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.