The grinding process is situated at the end of the machining chain, where geometric and dimensional characteristics and highquality surface are required. The constant use of cutting tool (grinding wheel) causes loss of its sharpness and clogging of the pores among the abrasive grains. In this context, the dressing operation is necessary to correct these and other problems related to its use in the process. Dressing is a reconditioning operation of the grinding wheel surface aiming at restoring the original condition and its efficiency. The objective of this study is to evaluate the surface regularity and dressing condition of the grinding wheel in the surface grinding process by means of digital signal processing of acoustic emission and fuzzy models. Tests were conducted by using synthetic diamond dressers in a surface grinding machine equipped with an aluminum oxide grinding wheel. The acoustic emission sensor was attached to the dresser holder. A frequency domain analysis was performed to choose the bands that best characterized the process. A frequency band of 25-40 kHz was used to calculate the ratio of power (ROP) statistic, and the mean and standard deviation values of the ROP were inputted to the fuzzy system. The results indicate that the fuzzy model was highly effective in diagnosing the surface conditions of the grinding wheel.

Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re-establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single-point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter 'counts' was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation.

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The monitoring of different machining processes has been studied for years, however many processes still do not have a final solution for their controls. The dressing, as it is of great importance in the finishing of workpieces produced through the grinding, is an operation whose monitoring becomes necessary. In order to make the dressing automation and, in this case, the process of dresser exchange, there is a need for efficient and lowcost monitoring. The vibration sensor has great potential, but it is still little used for this purpose. In this work the vibration sensor and neural models were used to classify the wear of dressing tools for three different conditions. Dry dressing tests and data acquisition were performed in a surface-grinding machine. The raw signals were further filtered in different frequency bands. Then, two statistics were computed, which served as inputs to the neural models. The results were quite satisfactory for some models.

Electromechanical impedance (EMI) technique has been employed in detection of structural failure in civil and mechanical structures because of its non-destructive property and easy implementation of small and inexpensive piezoelectric transducers that are attached to the structures, which lead to cost reduction as well as lesser dependence of manual inspection methods. In this technique, the capsule is excited by applying a sinusoidal voltage to generate waves to propagate throughout the structure. From the impedance signature of the structure without any damage, any structural change can be detected by measuring the electrical impedance of the piezoelectric (PZT) patch. Based on its real potentiality and because of its non-destructive characteristics, this work aimed to employ the EMI technique as the first alternative to monitor workpiece surface damages after grinding operation with a conventional abrasive grinding wheel. EMI measurements were performed by using a low-cost PZT transducer and under controlled environmental conditions. Microhardness and surface roughness of the machined surfaces, as well as grinding power, were also measured to detect any damage in the machined surface and to stablish relationship with the EMI technique. From the damage indices root mean square deviation (RMSD) and correlation coefficient deviation metric (CCDM), surface alterations on the ground surfaces were inferred by the EMI method. Also, it was observed a good correlation between the EMI technique and the other output parameters that were investigated in this work, such as surface roughness and power grinding, thereby posing as a non-destructive, low-cost, and viable technique to monitor workpiece surface damages in the grinding operation.

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One of the major challenges in grinding is to conciliate the material removal rate with components free from damages. Due to the poor thermal conductivity of conventional grinding wheels, most of the heat generated during grinding is transferred to the workpiece surface and subsurface, which can cause thermal damages and impair the performance in service of machined components. Hence, it is very important to monitor the grinding process to ensure the quality of the machined parts. Thus, this work presents an innovative study comparing two indirect monitoring methods in monitoring surface integrity of steel during grinding: the acoustic emission technique and the electromechanical impedance technique. Worktable speed and radial depth of cut were used as input parameters. Visual inspection and scanning electron microscopy images of ground surfaces as well as microhardness were the output variables used to assess surface integrity and to establish a relationship with the acoustic emission and electromechanical impedance techniques. Since the acoustic emission signals are non-stationary in nature, these signals were analyzed in the time–frequency domain by applying the short-time Fourier transform and the continuous wavelet transform. The root mean square deviation index was extracted as feature from the acoustic emission and electromechanical impedance signals. Results showed that both techniques presented similar results. The root mean square deviation index showed a good correlation with alterations in surface integrity under the conditions investigated.