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.

This paper presents a performance assessment of the low-cost thin-disk piezoelectric diaphragm (PZT) for surface integrity monitoring in the grinding operation. PZT diaphragms are flexible, low-cost, and smart devices, making them promising for applications in manufacturing fields, such as in the grinding operation. To accomplish this, experimental grinding tests were carried out on workpieces of ceramic material covering from slight to severe machining conditions. A low-cost thin-disk PZT diaphragm was assessed, which was attached to the workpiece holder using a cyanoacrylate glue. In addition, a commercial and consolidated acoustic emission (AE) sensor was used as a baseline for comparative purpose. Likewise, surface roughness measurements were performed after grinding each workpiece to support the sensor monitoring system. Feature extraction using frequency spectrum and time domain analysis were applied to the sensor signals by means of fast Fourier transform (FFT), digital bandpass filters, root mean square (RMS) value, and power deviation (DPO) metric. The results indicate a clear relationship between the thin-disk PZT diaphragm and the conventional AE sensor for surface integrity monitoring in grinding, which proves the effectiveness and reliability of an innovative sensing device for the present application.

The interest of the scientific community for ultrasound techniques has increased in recent years due to its wide range of applications. A continuous effort of researchers and industries has been made in order to improve and increase the applicability of non-destructive evaluations (NDE). In this context, the monitoring of manufacturing processes, such as the grinding process, arises. This work proposes a novel technique of ultrasound monitoring (chirp-through-transmission) through low-cost piezoelectric diaphragms and digital signal processing. The proposed technique was applied to the monitoring of material removal during the grinding process. The technique is based on changes in ultrasonic waves when propagated through the material under study, with the difference that this technique does not use traditional parameters of ultrasonic techniques but digital signal processing (RMS and Counts). Furthermore, the novelty of the proposed technique is also the use of low-cost piezoelectric diaphragms in the emission and reception of ultrasonic waves, enabling the implementation of a low-cost monitoring system. The results show that the monitoring technique proposed in this work, when used in conjunction with the frequency band selection, is sensitive to the material removal in the grinding process and therefore presents an advance for monitoring the grinding processes.