Sensing methods of tool wear are divided into direct or indirect measurements. The first method involves measuring the wear and evaluating the volumetric loss from the tool due to tool wear, in the second method a relationship is established between tool wear and other cutting parameters which are easier to measure.

Some of the direct tool wear sensors include, electrical resistance, radioactive and optical sensors. On the other hand, indirect tool wear sensors employ the measurement of other parameters which are indirectly related to tool wear. Some of these parameters are cutting forces, roughness of machined surfaces, temperature and thermoelectric effect, acoustic emission and vibrations [2].

This paper presents the results of investigations undertaken to determine if vibration signals can be used to detect failure in single point cutting tool. The tool wear monitoring using vibrations look prospective because of ease of measurement and ruggedness and sensitivity of sensors [4].

Cutting tests (turning) were carried out on high carbon steel (En 31) using uncoated indexable carbide inserts for regular intervals of time. Vertical tool vibration acceleration was measured using an accelerometer held on the tool holder at a safe distance away from the cutting process. The width of flank wear land was measured using Tool Makers Microscope. Tool acceleration amplitude at the first natural frequency of the tool, confirmed by impulse response testing is plotted against the width of flank wear land. This acceleration decreases at the beginning, approaches a minimum at the critical wear, and increases again. The trend remains unchanged under different cutting conditions, although the actual value changes. Based on this finding minimum tool acceleration can be used as an indicator of critical tool wear, and the acceleration can be used to monitor wear on-line [2]. REFERENCES

1. S. B. Rao, Tool Wear Monitoring Through the Dynamics of Stable Turning, ASME Journal of Engg. for Industry, 108, 183-190 (1986)
2. S. M. Pandit and S. Kashou, A Data Dependent Systems Strategy of On-line Tool Wear Sensing, ASME Journal of Engg. for Industry, 104, 217-223 (1982)
3. Li Dan and J. Mathew, Tool Wear and Failure Monitoring Techniques for Turning - A Review, International Journal of Machine Tools Manufacture, 30(4), 579-598 (1990)
4. M. Raghunandan and R. Krishnamurthy, Condition Monitoring Systems for Machining Applications, Proceeding sof COMADEM 90, Brunel University, July 16-18 (1990)