Among the methods used in structural health monitoring (SHM), the electromechanical impedance technique (EMI), which uses piezoelectric transducers of lead zirconate titanate (PZT), stands out for its low cost. This paper presents a new approach for monitoring of the dressing operation based on structural health monitoring from the digital processing of voltage signals based on the time-domain response of a PZT transducer by EMI method. Experimental tests of the dressing process were performed by using a single-point dresser equipped with a natural diamond. The voltage signals in the time-domain were collected in different damage levels using a measurements EMI System. By using damage metrics, it was possible to qualify different damage levels that the diamond suffered during the dressing operation, observing variations from the magnitude of the signals. The dressing operation is of utmost importance for the grinding process and the dresser wear negatively affects the result of the process, which owns high added value. In this way, this work contributes with a new monitoring tool which aims ensuring a consistent dressing operation.Keywords: Manufacturing process, automation, electromechanical impedance, dressing operation.

Grinding is a high-precision, high-value-added finishing process as it is usually the last stage of the manufacturing chain. However, unsatisfactory results may occur, mainly due to changes in the microstructure of the ground workpiece. Such changes are caused by the high temperatures involved in the process due to the grinding conditions in which the part was subjected. In this way, the main objective of this work is the monitoring of the grinding process in order to detect changes in the signal and to relate them with damage occurred in the ground workpiece. The tests were carried out on a surface grinding machine, aluminum oxide grinding wheel and ABNT 1045 steel parts. Metallography was performed on the parts for a more further analysis of their microstructure. The recording of signals was obtained at a sample rate of 2 MHz through an acoustic emission sensor (AE). A frequency study for the selection of the best frequency bands that characterize damage occurred in the ground workpiece. The event counts statistic was applied to the filtered signal in the chosen frequency bands. The results of this work show that the grinding conditions influence the signal and, therefore, its frequency spectrum.Keywords: Manufacturing process; automation, monitoring; grinding process; acoustic emission, damage detection