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Numerous developments have been witnessing in manufacturing industries the ability of the machines to change the tools automatically during their wear or damage. In general, tool failure contributes about 7% to the down time of machine centers. Therefore, online monitoring of tool wear is an important phenomenon in producing quality products at reasonable cost. This also increases the production rate in the industries. Tool condition monitoring using the acoustic emission technique (AET) are real methods identified by researchers for online quality assessment of machine tools. The genetic algorithm (GA) is used to optimize the tool wear rate parameters. The practical significance of applying GA to tool wear rate has been validated by means of computing the deviation between predicted and experimentally obtained process parameters. Based on this research work, an experimental setup has been developed for online monitoring of a single point cutting tool using AETs. The experimental tool wear rate results are compared with online measurements using mean acoustic emission parameters (average value, root mean square value and area).

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