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The great challenge of modern industry is to carry out an online prediction in the shop floor during the machining to define the exact tool breakage instant and simultaneously improve the quality of manufactured products. Acoustic emission sensors have been used to monitoring traditional and non-traditional machining processes. This work shows a study of the online monitoring in the microturning process using an acoustic emission sensor. A factorial design was performed to examine the effect of the feed rate, depth of cut, cooling system, and the type of tool on the response acoustic emission signal. Moreover, the acoustic emission signal was correlated with surface roughness and microhardness. The results showed that the acoustic emission signals are sensitive with the progressive increase in surface roughness and the microhardness.

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