Abrasive Waterjet Cutting (AWC) is acknowledged as a leading manufacturing technology in numerous high-end applications (e.g., aerospace, defense, automotive). AWC is characterized by several interesting features including its relatively low initial investment, no heat-affected zone on the workpiece, no limitations in shape complexity, the absence of mechanical contact with physical tools making it very delicate on fragile and/or composite materials, narrow kerf (down to 0.3 mm), negligible burrs, good edge sharpness [1]. All these features provide an edge with respect to alternative technologies. However, power-user segments require cutting-edge performances from their manufacturing processes, in terms of stability and product assurance, to tackle very stringent safety and quality standards. Indeed, complying with such requirements is a relevant challenge for AWC, because of unstable process conditions and limited monitoring and control capabilities of current machines [2]. The present investigation lies in the field of vibroacoustic process monitoring, as a means for extracting relevant information that could benefit AWC on these instances.
The cutting head, i,e, the terminal component of an AWC system, is responsible for guiding the cutting jet towards the workpiece and is shown in Fig. 1: water is fed and controlled by means of an intensifier or direct drive pump with the pressure typically ranging between 300 and 400 MPa, although pumps up to 600 MPa are available nowadays. The water flows through the primary orifice and here pressure is converted into kinetic energy, producing a high-speed (about 1000 m/s) waterjet. Downstream, the mixing chamber receives a mix of abrasive particles and air, at a rate that typically ranges between 100 and 400 g/min (although lower rates down to 3–5 g/min can be used in high-precision applications [3]) and is controlled by various types of feeders (belt-based, screw-based and others). The resulting multiphase jet flows through the focusing tube, which is responsible for transferring momentum from the water to the particles. After leaving the focusing tube, the multiphase jet gets airborne and subsequently impacts on the workpiece, resulting in a material removal process [4].
In AWC, the Computer Numerical Control (CNC) software is responsible for controlling several process variables, the most relevant being the water pressure (p), the abrasive feed rate (ma), the traverse speed (or feed rate) and the standoff distance. The standoff distance is the distance between the focusing tube’s tip and the workpiece; it is typically maintained between one and two millimetres, which constitutes the optimal range for most of the applications. The traverse speed, p and ma are selected according to CNC performance models that are designed for maximising productivity; more in detail, the traverse speed is generally pushed to the highest value that complies with the target quality of the kerf walls (basically the surface roughness and the kerf taper), while the p and ma setpoints correspond to the maximum values that can be delivered by the hardware.
The kinetic power Ppart is an important variable that defines the cutting capability and has an impact on several process Key Performance Indicators (KPIs). This variable becomes even more critical in applications requiring top-notch accuracy and absolute integrity of the cutting edge, e.g. no cracks on fragile materials. The theoretical definitition of Ppart corresponds to the kinetic power of the abrasive particles, being the only phase that contributes to the material removal process. The theoretical derivation of Ppart is reported in Section 2 of the present paper, as well as its correlation with p and ma, which are the two process parameters that are used for its control. Machine builders have delivered substantial efforts for stabilizing Ppart, in an attempt to improve productivity and the final product quality. Indeed, further variables besides p and ma concur to its fluctuation and drift versus time; these include the instantaneous fluid-dynamic conditions inside the mixing chamber, which tend to fluctuate, the instantaneous feeding rate from the abrasive line [6], which is affected by a relevant instability, and the instantaneous focusing tube’s inner diameter, which increases due to the wear phenomena [7][8], as it accumulates operating hours. Having an in-line Ppart indicator available could enable the implementation of closed-loop controls of p and ma specifically aimed at compensating said fluctuations. Unfortunately, the monitoring infrastructure of current AWC machines cannot deliver this information. Such deficiency has pushed the research towards the implementation of innovative monitoring techniques that can enrich the available process dataset. The present investigation is intended to demonstrate how the AWC airborne acoustic emission can be used for extracting a selective and robust acoustic signature of the waterjet, from which an in-line Ppart indicator can be derived. The subsequent part of this section presents a literature survey about vibroacoustic process monitoring, with a focus on AWC applications.
The AWC operational vibroacoustic emission has been the object of several attempts, aimed at extracting relevant process information. The literature survey indicates that these methods can be categorized into two groups: a first, in which the objective is to monitor process and workpiece variables, including the final quality; a second, with a greater focus on the diagnostics and condition monitoring of components. The first category of methods relies on the extraction of synthetic indicators from the monitored signals, which are proven to correlate with the target variables.
In [9], the authors studied the acoustic emission during AWC of AISI 1018 carbon steel, with the aim of monitoring the instantaneous cutting depth. Measurements were carried out by means of two acoustic sensors attached on the workpiece and in the proximity of the cutting area. Subsequently, the Root Mean Square (RMS) of the signals was proven to be linearly proportional to the cutting depth, thus constituting an in-line indicator of this important process outcome.
In [10], AWC experiments were carried out on aluminium alloy sheets using various standoff distances and maintaining the other process parameters constant. The operational acoustic emission was monitored by means of a microphone. The signal processing consisted in the computation of two indices, namely the RMS and a power spectrum integral, both of which were proven to be linearly proportional to the standoff distance, for limited thicknesses of the workpiece.
In [11], the authors measured the operational acoustic emission during AWC operations and nearby the cutting head. Subsequently, the signals were proven to be correlated with the transverse speed and this conclusion enabled the implementation of a system for closed-loop control and supervision over the workpiece quality.
In [12], the authors used four accelerometers for measuring an AISI 309 stainless steel workpiece vibration, during AWC operations at various ma. The signals were processed by means of a Fourier Transform and a correlation between the workpiece quality and certain spectral amplitudes was subsequently proven.
In [13], an analogous experimental setup to the one of [12] was used for demonstrating the correlations of the workpiece vibration with ma, the transverse speed and the focusing tube’s inner diameter.
In [14], the authors carried out an experimental investigation, in which Composite Fiber Reinforced Panel (CFRP) materials were cut by means of AWC and the acoustic emission monitored by means of two sensors, of which one installed on the cutting head and the other on the workpiece. In the conclusions, the authors observed a correlation between the transverse speed (hence the surface roughness) and the signals’ amplitudes.
In [15], AWC experiments on CFRP, titanium, and CFRP-titanium stacks were carried out, with the aim of correlating the vibroacoustic emission with p and the traverse speed. This investigation was successful in identifying frequency ranges with a detectable sensitivity to the target variables. Among further conclusions, the authors mentioned the possibility of using this method in innovative strategies for process control and troubleshooting.
In [16], the authors carried out AWC experiments on aluminium 5251 panels, at different traverse speeds. The impact of this parameter on the surface quality was assessed. Synchronously, the acoustic emission originated from the workpiece was measured at a very high frequency (1 MHz) and recorded. Subsequently, the signals were analysed in the frequency domain and proven to be correlated with the surface quality.
In [17], AWC of titanium-CFRP stacks was carried out at various p and ma, and the operational acoustic emission monitored and subsequently processed by means of a wavelet decomposition method. Among the conclusions, the time-localized nature of the wavelet filters was proven to be an effective feature in extracting relevant process information from the signals.
The AWJ drilling of Inconel 718 and AISI 1040 steel was the object of experimental investigation in [18]. Here the authors successfully proposed the operational acoustic emission as a tool for monitoring the penetration depth, as well as characterizing the type of worked material. The same method was also proven effective in identifying non-compliant worked pieces, by means of comprison against benchmark acoustic data.
Further investigations specifically tackle the correlation of operational vibroacoustic emission with energy performance indicators of the AWC process. Herein, the target KPIs mostly consist in the jet input energy and its active fraction, i.e. the amount that provides an effective contribution to the cutting process (hence correlates with the cutting depth). In [19] it is presented a first attempt in the direction, in which a monitoring setup consisting of two acoustic sensors at different locations was successfully exploited for monitoring the active energy, as well as delivering a penetration depth estimator and further troubleshooting data. An analogous study is reported in [20]: here a similar setup was used for measuring the active energy and the information exploited in a subsequent step, for feeding innovative closed-loop controls of p and ma.
The AWC operational vibroacoustic emission has been exploited as a source of relevant information for process diagnostics and condition monitoring, as well. In [6], the authors assert that information can be extracted from the monitored signals, which is related with the health status of the primary orifice and the focusing tube. The prior-art shows a variety of methods relying on AWC operational vibroacoustic emission and dealing with the monitoring of wear progressions.
The investigations reported in [21][22][23] were specifically intended to correlate the focusing tube’s wear status with the AWC operational acoustic emission. Experimental campaigns were carried out by using focusing tubes with different inner diameters and measuring the operational acoustic emission, which was subsequently analysed in the frequency domain. In the conclusions of the study, a frequency range was identified at about 20 kHz, in which the power spectral amplitudes were proven to be sensitive to the focusing tube’s inner diameter.
In [24] it is presented a further experimental study that deals with the monitoring of the focusing tube’s wear status and makes use of a setup analogous to the one used in [13], consisting of four accelerometers attached to the workpiece. Vibration signals were gathered during AWC operations and analysed in the frequency domain. The conclusions identified characteristic spectral peaks and demonstrated their sensitivities to both the focusing tube’s inner diameter and ma.
In [7], the AWC operational vibration was monitored by means of one accelerometer installed at the focusing tube’s tip; the delivered signal successfully enabled a tracking of the focusing tube’s resonant frequency and the latter was proven to constitute an effective wear status indicator.
The outlined literature survey seems to confirm the effectiveness of vibroacoustic monitoring as a means for extracting relevant process information that could help tackling the current AWC limits and issues. In [5] one further setup is discussed, which appears particularly relevant to the aim of the present dissertation: here the authors made use of a special focusing tube, hosting two accelerometers on its tip; an experimental study was conducted, in which the jet was fired at various p and ma; the operational vibration was monitored by means of the two accelerometers, and subsequently analysed in the frequency domain. A hypothesis was made, in which one particle impact in the focusing tube’s inner bore triggers a single vibration response that is quantitatively proportional to the particle’s kinetic energy and the overall vibration is the sum of the single responses per unit of time. The hypothesis was confirmed by the correlation of high-frequency vibration amplitude with Ppart, notably above 10 kHz; a much lower correlation was found at lower frequencies. The discriminant factor among the two frequency ranges was identified in the types of vibration modes involved: whilst the high-frequency range only includes local modes of the focusing tube, the low-frequency range appears affected by global modes of the AWC system, which do not bring relevant information for the purpose of Ppart monitoring.
Indeed, the method detailed in [5] was proven effective in delivering a reliable Ppart in-line indicator. However, the method relies on the deployment of sensing hardware at the very tip of the focusing tube, which constitutes a critical location, given its proximity to the jet impinging point. On the other hand, a method based on a sensors’ deployment further away from the jet could represent a more robust and user-friendly setup from the end-user perspective, hence providing greater potential for market success. In the present investigation, the authors intend to monitor Ppart by means of the airborne acoustic emission, measured with a condenser microphone installed on the cutting head. Factorial studies are presented, in which p and ma are varied among different set points and the acoustic emission monitored and processed in the frequency domain. The monitoring setup appears much simpler compared to [5] as it does not require the installation of contact sensors, as well as more robust, being the microphone located further away from the jet impinging point. Conclusions of this study are partially coherent with [5] as a robust correlation between Ppart and the measured acoustic emission is found, above 40 kHz. Such high-frequency data is proven to constitute a robust and selective acoustic signature of the airborne jet, relatively unaffected by input disturbances and with good measurement reproducibility. Overall, the presented method appears effective in monitoring p-induced variations of Ppart, whilst the impact of ma remains undetected. The method is expected to represent a valuable tool for supporting innovative closed-loop controls of the water pump, which could help tackling the end-user requirements for improved process stability.
The present contribution is structured as follows: in Section 2, the theoretical definition of Ppart is presented; in Section 3, the materials and methods are introduced; in Section 4, results are presented and discussed; conclusions are drawn in Section 5.