**1. Introduction**

The manufacturing processes convert raw materials into finished products, often employing machines or machine tools [1]. Among the finishing processes, grinding is considered one of the most important processes because it is applied in the final stage of the production line, providing the final finishing of the part [2].

In the grinding process, the material removal mechanism is performed by the contact between the abrasive surface of the grinding wheel, which is composed of thousands of abrasive grains with undefined cutting geometries randomly distributed [3], with the workpiece surface, which makes grinding a highly stochastic process [4]. The monitoring of the grinding process allows a better understanding of the interaction between the grinding wheel abrasive grains and the workpiece surface [5]. In addition, a better understanding of the material removal mechanism can help in the control of the grinding wheel wear and the workpiece surface quality [6].

The monitoring methods used in manufacturing processes are usually separated into two major categories—direct and indirect [7]. Direct methods are usually employed in a laboratory environment

because of their intrinsic handling limitations and are less used in industrial environments. On the other hand, indirect methods are more employed in industrial applications because of their easy handling and application, which are very important characteristics in this type of monitoring [8].

In the indirect methods, sensors are employed to measure a certain variable of interest. Among the sensors employed, the most common is the acoustic emission (AE) sensor, used by Nascimento Lopes et al. [9] in the monitoring of the dressing process; the power sensor, used by Chen et al. [10] in the study of the energy efficiency of an industrial cutting process; the vibration sensor (using accelerometers), used by Dimla [11] in the study of the cutting tool wear; and the force sensor (using dynamometers), used by Agarwal et al. [5], along with the acoustic emission sensor, in the monitoring of the grinding process.

The piezoelectric diaphragm (buzzer) is presented as a low-cost alternative to the traditional components used in structural impedance analysis. Freitas et al. [12] studied the feasibility of applying this transducer as an alternative to commonly used transducers in electromechanical impedance (EMI) methods. Budoya et al. [13] employed the piezoelectric diaphragm in the structural health monitoring system (SHM) to detect structural damages through the impedance signatures, acquired during the evaluating process. The piezoelectric diaphragm was also used as an alternative to the traditional vibration sensor (accelerometer), as presented by Lucas et al. [14], in the detection of structural anomalies in induction motors. In addition to this type of monitoring, the piezoelectric diaphragm was used in the monitoring of manufacturing processes, as presented by Marchi et al. [15], in which the electromechanical impedance method was implemented by means of piezoelectric diaphragms in the evaluation of the workpiece surface quality in the grinding process. In Ribeiro et al. [16], the piezoelectric diaphragm was employed to detect the burning phenomenon in ground workpieces, obtaining good results in the use of this transducer as an alternative to the commercial AE sensor.

This work proposes a new method for monitoring the material removal mechanism in the grinding process by means of the application of piezoelectric diaphragms, employing an alternative through-transmission technique, referred to as chirp-through-transmission ultrasound technique, along with digital signal processing techniques. The present work is an expansion of the research described in Alexandre et al. [17], where initial results were presented. In addition to the use of low-cost piezoelectric diaphragms in the emission and reception of ultrasound waves and the chirp signal as the input to the emitter transducer, what differentiates the proposed technique from others is the digital signal processing of the received signal (RMS and Counts) within a selected frequency band instead of traditional parameters of ultrasound techniques. Thus, this work paves the way to new possibilities for monitoring the material removal mechanism in the grinding process as well as for its automation. It is worth mentioning that the technique presented in this work is a novel approach to the monitoring of the grinding process, as no similar studies were found in literature.
