*Article* **Advisability-Selected Parameters of Woodworking with a CNC Machine as a Tool for Adaptive Control of the Cutting Process**

**Richard Kminiak 1 , Miroslav N ˇemec 2, \* , Rastislav Igaz <sup>2</sup> and Miloš Gejdoš 3**


**Abstract:** The operation of CNC machining centers, despite their technological progress, can still be affected by undesirable events associated with the technological parameters of their operation. The minimization of these risks can be achieved via their adaptive control in the process of operation. Several input parameters for adaptive control are still the subject of research. The work aimed to find out the influence of the change in feed speed, revolutions, and radial depth of cut on the noise and temperature of the tool during the milling of wood-based composite material particleboard. At the same time, it was evaluated whether it is possible to use the measured values of these parameters in the future in the process of an adaptive control of the CNC machine with the minimization of their negative influence. The methods of measuring these parameters were chosen based on valid legislation and previous research. The results of the research show that all parameters influence both the noise and temperature of the tool, while the rate of the radial depth of cut has the greatest influence on the increase in temperature, and the noise is most affected by the revolutions. The effect of temperature during woodworking can also be characterized in terms of the potential long-term wear of the cutting tool. The setting of optimization algorithms of monitored parameters in the adaptive control of the CNC machining center will be the subject of further research.

**Citation:** Kminiak, R.; Nˇemec, M.; Igaz, R.; Gejdoš, M. Advisability-Selected Parameters of Woodworking with a CNC Machine as a Tool for Adaptive Control of the Cutting Process. *Forests* **2023**, *14*, 173. https://doi.org/10.3390/f14020173

Academic Editor: Stefano Grigolato

Received: 22 December 2022 Revised: 13 January 2023 Accepted: 14 January 2023 Published: 17 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Keywords:** CNC machines; acoustics emission; wood-processing parameters; tool temperature; adaptive control

#### **1. Introduction**

Universality, high precision, and product quality achieved through Computerized Numerical Control machining centers (CNC machines) have caused their dynamic development in practically all production spheres of industrial production [1–3]. These devices are also financially available for small- and medium-sized enterprises. The producers of these machines are looking for modern approaches to increase productivity and efficiency [4]. Modern CNC control units are designed to deal with obvious threats such as overload (via various sensor systems) and collision (by checking the syntax of the NC code and simulating it on the machine). However, despite these safety systems, various undesirable events still occur during the machining with CNC machining centers. This means that there are still risks that can seriously affect the operation of CNC machining centers [5]. Minimization of risks in the operation of CNC machines can be achieved by several approaches. A modern approach in this direction also represents the adaptive control of CNC machines based on achieved parameters in the process of operation, using neural networks and applying fuzzy logic approaches [6–10]. Since the machines work in different operating conditions with different materials, the risks of their operation can differ significantly. However, the basic risk factors from the point of view of machine operators include noise,

vibrations, and from the point of view of their operation, the temperature of the cutting tool and the machine [11,12]. When the CNC machine is turned on, the working spindle with the tool, which rotates at a high cutting speed, produces noise. When in contact with the material, this noise increases significantly. It is also important to how the machine works and what material it processes. For example, in climb milling, the noise is greater than in conventional milling. The feed speed also has a great influence on the noise of the CNC machine [13–15].

The implementation of the Industry 4.0 philosophy, as well as the need to increase the efficiency of production processes, creates pressure for an increasingly greater degree of automation and leads to the minimization of the worker's role in the working process. It is becoming standard that one operator serves several production facilities and they perform production operations autonomously. The worker performs only supervision, and his main role has become interoperation control. Here, space is created for the application of control and safety mechanisms that will replace the original role of the operator-to eliminate the occurrence of unfavorable situations during the autonomous operation of the production equipment itself.

One of the ways to solve this problem is the collection of all available continuously measurable physical values and their subsequent interpretation using cyber-physical systems. The physical value that reacts more sensitively and faster to changes in the given process has a higher informative value. Our work aimed to assess the response of the tool temperature and the noise of the machining process to the change in the parameters of the cut layer.

Problems with the operation and damage of tools, or in the case of wood and wooden materials, the deterioration of the machined material also, can also be caused by high temperature, which arises as a result of high revolutions, cutting speed, feed, tool and workpiece materials, etc. In this direction, the temperature distribution on individual parts of the machine, tool, and workpiece, or the use of coolant, is important. Keeping the tool temperature below the critical level consequently extends its life [16–18]. Part of the mechanical energy consumed during cutting and milling is converted into thermal energy due to friction [19]. Unlike metals, the thermal conductivity of wood is low. Therefore, most of the heat generated during milling is transferred to the tool, which leads to its increased temperature [20–22]. The influence of the temperature of the tool during the cutting process on the service life of the tool was dealt with by many authors who discovered the influence of several parameters of the machining process, the properties of the tool, and the material being machined. Some dealt with the issue experimentally, others analytically or numerically, e.g., [23–25].

The current trend of setting and programming algorithms for CNC machines is for the machine to be able to evaluate the critical values of the given parameter and accordingly adjust the production cycle by adjusting the operating conditions. These conditions are met using the process of adaptive control [26,27]. The first step for the implementation and creation of an adaptive control system is the interpretation of the measured critical parameters into the creation of a structural model [28]. Such a system also helps in optimizing the time schedule of preventive maintenance of the machine with an effective cost allocation [29,30].

Published works in this area focus mainly on research into the factors of wear of cutting tools with selected cutting parameters. At the same time, adaptive control currently relies mainly on mathematical models and not on direct evaluation of critical parameters of the cutting process.

The aim of the work was to determine the influence of process parameters affecting the size of the cut layer (revolutions, feed speed, and radial depth of cut) on the temperature of the cutting tool and sound pressure level (A)-SPL (A) during the milling of the woodcomposite particleboard (PB). At the same time, it was evaluated whether the obtained measured values of these parameters can be used in the future in the process of adaptive control of the CNC machine with the minimization of their negative impact.

#### **2. Materials and Methods**

#### *2.1. Used Material and CNC Machine*

Particleboards (PB) supplied by the company Kronospan Ltd., Zvolen, Slovakia, were used in the experiment. The density of the material given by the manufacturer is 600 kg·m−<sup>3</sup> – 640 kg·m−<sup>3</sup> (deciduous 10%, coniferous 90%), and urea formaldehyde glue with paraffin admixture is used. The manufacturer declares that the material complies with the EN 14322 standard, EN 312-2, and emission class E1 (EN ISO 12460-5). These PB had a thickness *h* = 18 mm, width *w* = 2800 mm, length *l* = 2070 mm. Within the experiment, a modified particleboard format of 1000 mm × 1000 mm was used. For each combination of considered parameters, 5 samples were used [31–33]. −3 −

CNC machine specification:

The experiment took place on a 5-axis CNC machining center SCM Tech Z5 (Figure 1). Table 1 provides the basic technical–technological parameters given by the manufacturer of this machine.

**Figure 1.** CNC machining center SCM Tech Z5.

**Table 1.** Technical parameters of CNC machining center SCM Tech Z5.


#### *Tool Characteristics:*

The experiment used a diamond-end mill IGM Fachmann Ekonomik Z2+1 from the manufacturer of IGM tools from the Czech Republic (Figure 2). *Milling-Cutter parameters*: working diameter 16 mm, working length 26 mm, the diameter of the clamping head 16 mm, the direction of rotation-right, maximum permissible revolutions 24,000 rpm−<sup>1</sup> . This milling cutter was installed in hydraulic clamp SOBO 302680291 GM 300 HSK 63F, manufactured by Gühring KG from Germany.

**Figure 2.** Diamond shank-type cutter IGM Fachmann Ekonomik Z2+1.

Parameters of milling process:

−

We carried out the high-speed milling process under the technological conditions listed in Table 2. As a basis for choosing the technological parameters of the experiment, the parameters recommended by the tool manufacturer were used. The revolutions were used from 75% to 100% of the recommended, the radial depth of cut values were in the range of 1 2 and 1 4 of the tool diameter, and the feed speed was in the range of ±33% of the value recommended by the manufacturer.

**Table 2.** Parameters of milling process.


#### *2.2. Noise Measurement Methodology*

The measurement was carried out in the enclosed places of the machining hall of the Technical University in Zvolen and was carried out under the same conditions that workers are exposed to during their daily work. A Brüel & Kjær type 2270 sound level meter (Denmark) was used for the measurement, which made it possible to measure and indicate sound pressure levels. It is a two-channel hand-held sound analyzer classified as accuracy class 1 with a Brüel & Kjær 4189 microphone. Its calibration was performed with a Brüel & Kjær 4231 calibrator. The sound level meter was placed on a tripod at a height of 1.50 m behind the yellow line on the ground, which is at 1 m from the CNC machine and serves as a safe distance for the worker, which he must not exceed during operation. The distance of the sound-level meter from the place of machining was 2 m [34]. The main descriptor was the equivalent sound pressure level *A* emitted by the CNC machine tool during milling, depending on the revolutions, the radial depth of cut, and the feed speed (Table 2).

During the measurement, the equivalent *A* sound pressure levels were recorded when the CNC machine was switched on, including the time when it was not under workload. During subsequent processing with the software Measurement Partner Suite (MPS–BZ 5503) (Brüel & Kjær, Nærum, Denmark), transitions were selected under the same operating

conditions, which were repeated 3 times. The energy mean of these three values was calculated according to Formula (1) [34]:

$$L = 10\log\frac{1}{n}\sum\_{1}^{n}10^{0.1\times L\_i} \,\text{s}\tag{1}$$

∑

where:

*L* is the energy mean of the equivalent sound pressure level *A* (dB), *n*—number of measurement repetitions (3),

*Li*—sound pressure level (*A*) of the *i* measurement (dB).

#### *2.3. Temperature Measurement Methodology*

The temperature measurement in the process of tool–workpiece interaction was carried out non-contact by means of two pieces of Raytek MI3 LTF pyrosensors (Raytek, Berlin, Germany) connected to the computer through the RAYMI3-MCOMMN communication interface (Raytek, Berlin, Germany). The layout of the measuring equipment is in Figure 3. The sensors allow non-contact temperature measurement with an optical resolution of 10:1 in the range −40 to 1000 ◦C in the wavelength range of 8 µm to 14 µm. The sensors were placed at 100 mm from the surface of the workpiece, perpendicular to the machined surface at a mutual axial distance of 50 mm in the direction of movement of the tool. The diameter of the scanned area on the surface of the workpiece is 10 mm in this case. This arrangement allows two tool-temperature measurements to be made in one tool pass. Data recording was performed at a frequency of 30 Hz, which allowed the recording of 5 images of the tool in the field of view of the sensor at the largest feeding speed. The emissivity of the scanned surface was set to 0.9. The tool temperature was measured after milling the 900 mm track, where the temperature conditions in the tool–workpiece environment system were stabilized. The tool temperature was determined as the maximum recorded temperature in the field of view of the sensors. The resulting temperature was determined as an average of 10 measurements. −4

**Figure 3.** Layout diagram of pyro sensors for temperature measurement.

#### **3. Results**

#### *3.1. Noise*

In Figure 4 is the dependence of sound pressure level (A)–SPL (A) on revolutions at different feed speeds (gray 8 m·min−<sup>1</sup> , orange 6 m·min−<sup>1</sup> , blue 4 m·min−<sup>1</sup> ) and different radial depth of cuts.

−1 −1 −1 From Figure 4, the sound pressure level (A) of the CNC machine increases with increasing feed speed (in the range of 15,000 min−<sup>1</sup> to 20,000 min−<sup>1</sup> ) during all radial depth of cuts. This is since the dominant source of noise is the aerodynamic noise caused by the rotation of the CNC machine spindle. As the thickness of the removed chips only slightly

decreases with the increase in revolutions, this phenomenon does not significantly affect the noise level. As expected, the highest noise is at the highest feed speed of 8 m·min −1 , when the tool removes the largest amount of material per unit of time (significant increase in chip thickness) and, on the contrary, the smallest at a feed speed of 4 m·min −1 .

— — — **Figure 4.** Dependence of sound pressure level (SPL) on change in revolutions, feed speed, and radial depth of cut ((**a**)—radial depth of cut 4 mm, (**b**)—radial depth of cut 6 mm, (**c**)—radial depth of cut 8 mm).

−1 −1 −1 −1 On Figure 5 is the dependence of the sound pressure level (A) on the revolutions at different material radial depth of cuts (grey 8 mm, orange 6 mm, blue 4 mm) and different feed speeds. Even in this case, it is possible to deduce from the graph that the noise of the CNC machine increases with increasing revolutions at all feed speeds. The highest sound pressure level (A) was measured at the highest-used radial depth of cut of 8 mm when there was the most pronounced interaction between the CNC milling cutter and the workpiece. This is caused by the fact that the active length of contact between the tool and the workpiece increases significantly when the material radial depth of cut increases. From the analysis of the processed data, it is clear the SPL (A) value when the cutter is not in engagement is at least 12 dB lower than it is in engagement for all set parameters. Its value ranges from 68 dB to 78 dB. Therefore, this value does not significantly affect the measurement results. The value of the extended measurement uncertainty ranges from 1.6 dB to 1.8 dB.

The results of the SPL (A) measurement at individual operating conditions show that the tonal components are, as expected, integer multiples of revolutions. At 15,000 revolutions per minute, the frequencies are 250 Hz, 500 Hz, and 1,000 Hz, and at 17,5000 and 20,000 revolutions, the frequencies are 315 Hz, 630 Hz, and 1,250 Hz. Considering that it is a milling cutter with two cutting edges, the highest tonal components are at frequencies of 500 Hz and 630 Hz (Figure 6).

—

−1 – **Figure 6.** FFT analysis for 20,000 revolutions, feed speed 8 m·min −1 , and radial depth of cut 8 mm. The tone components are 315 Hz, 630 Hz, and 1,250 Hz, and the SPL (A) value at 630 Hz is 84,6 dB (processed using Measurement Partner Suite software–Brüel & Kjær, Denmark).

#### *3.2. Temperature*

−1 – – −1 −1 – −6 −1 – – −1 −1 – −6 −1 – On the graphs of the dependence of the temperature of the tool on the revolutions at a constant value of radial depth of cut and feed speed (Figure 7), the influence of the feed speed on temperature increase is relatively small. The temperature of the tool changes only slightly with the increase in revolutions, which is since the thickness of the removed chip (from 4 mm to 8 mm) only slightly decreases with the increase in revolutions. Wood has a low value of thermal conductivity (0.10–0.25 W·m−<sup>1</sup> ·K−<sup>1</sup> ) and thermal diffusivity (0.15–0.25·10 <sup>−</sup><sup>6</sup> m<sup>2</sup> ·s −1 ) in the direction perpendicular to the fibers [35–37], which causes slow heat transfer in the wood. Increasing the revolutions of the tool causes the thickness of the chip to decrease, and therefore the tool separates a thinner layer at the next contact of the blade with the material, which, due to thermal conductivity and diffusivity, has a higher temperature. The result is a slight increase in the temperature of the tool with increasing revolutions. However, this influence is not significant, because the thickness of the chip changes only slightly. When the tool speed changes from n = 15,000 min −1 to −1

−1

−1

n = 20,000 min −1 , the mean thickness of the chip decreases by approximately 25% (e.g., at feed speed *v<sup>f</sup>* = 8 m·s <sup>−</sup><sup>1</sup> and radial depth of cut *a<sup>e</sup>* = 8 mm, it is from 0.189 mm to 0.141 mm). " "

−1

−1

— — — **Figure 7.** Dependence of temperature on change in revolutions, feed speed, and radial depth of cut ((**a**)—radial depth of cut 4 mm, (**b**)—radial depth of cut 6 mm, (**c**)—radial depth of cut 8 mm).

A more significant trend that can be seen from the graphs is that the temperature of the tool decreases with the increasing value of the feed speed. This trend is caused by a more significant increase in chip thickness (when the feed speed changes from *v<sup>f</sup>* = 4 m·s −1 to *v<sup>f</sup>* = 8 m·s −1 , the average chip thickness increases from 0.071 mm to 0.141 mm, i.e., by 100%), which, due to the small value of thermal conductivity and diffusivity, causes the cutting edge to enter the colder material during the next cut of the tool. The length of the friction surface, when the tool separates the chip, remains unchanged when the feed speed is changed. The result is a temperature decrease in the tool, which is more effectively cooled by the "cold" material than heated by the friction of the tool against the material. This trend is best observed at the largest value of the radial depth of cut.

Other graphs show the dependence of the tool temperature change on the radial depth of cut (Figure 8). It is clear from the trends that increasing the radial depth of cut significantly affects the increase in tool temperature. The reason is the fact that as the radial depth of cut increases, the contact length of the tool with the material increases significantly, which results in a longer friction surface and a significant increase in the produced heat (at a radial depth of cut of 4 mm, the length of contact between the tool knife and the workpiece is 8.38 mm, and at a radial depth of cut of 8 mm increases to 12.57 mm, i.e., by 50%). This trend is clearly observable in all cases and is further synergistically supported by increasing the revolutions, which causes a decrease in the thickness of the chip and thus an increase in the temperature at the point of the cut due to the conduction of heat generated during the previous cut in the material. Confirmation of the radial depth of cut as the most important parameter affecting the temperature increase is also confirmed using statistics (Table 3). The standard deviation was less than 2.0 ◦C in all measurements.

— — −1 — −1 **Figure 8.** Dependence of the temperature change in the tool on the radial depth of cut and revolutions ((**a**)—feed speed-4 m·min −1 , (**b**)—feed speed-6 m·min −1 , (**c**)—feed speed 8 m·min −1 ).

**Table 3.** The value of the correlation coefficient rxy between the machine parameters and the measured temperature and noise parameters.


– Table 3 shows the correlation coefficients of noise and temperature depending on the change in the operating parameters of the CNC machine. Sound pressure level (A) showed the highest dependence on revolutions, while the extent of the radial depth of cut did not show a significant effect. In adaptive control, it is, therefore, necessary to focus mainly on the dependence of the sound pressure level (A) on the level of the revolutions for the selected type of tool and workpiece.

− The tool–workpiece interaction temperature showed the greatest dependence on the radial depth of cut setting. As part of the adaptive control, it is, therefore, necessary to find a balanced ratio between the set radial depth of cut and revolutions, where the optimal value for both measured parameters appear to be between 15–17 thousand revolutions and radial depth of cut of 6 to 8 mm for the selected type of tool and workpiece. The dependence of temperature on feed speed is statistically confirmed only in an indirect plane. Thus, feed speed affects the temperature partly indirectly (negatively).

#### **4. Discussion**

The methods of measuring and evaluating tool temperature in CNC machines were objectified by Xie et al. (2009) [38]. Based on these principles, an objectified methodology for measuring the temperature of the tool was chosen. The same trends of the dependence of the tool temperature on the change in monitored parameters were identified in the work [16]. However, tool temperature does not appear to be a major problem in woodworking. The problem is rather in the machining of metallic materials due to the energy management of the machine, where the design of cooling mechanisms is also necessary [17]. In woodworking, the use of coolants and lubricants has proven impractical. Gisip et al. [39,40] demonstrated that the application of cold air or heat treatment of the tool before processing wood-agglomerated materials can have an impact on the overall life of the tool. Our results thus indirectly confirm that even if the temperature of the cutting tool during woodworking does not move within critical values, the application of this parameter in the adaptive control of CNC machines has significance. As part of the conducted research, the measured temperatures do not approach the "critical values". The critical temperature is considered to be approx. 400 ◦C, when there are visible changes in the color of the wood surface.

The parameter sound pressure level (A) has not been shown to be fundamental in terms of wear of the cutting tool [41]. In woodworking, noise as a more fundamental problem from a hygienic point of view was manifested earlier in sawmill wood processing and on technologically older machines than in CNC woodworking [42,43]. Monitoring the sound pressure level parameter (A) has, however, the importance in terms of its changes with a connection to the change in the cutting parameters of the machine and the tool used. Fluctuations in noise when changing cutting parameters can have a negative impact on the machining process in a long-term context. Therefore, it is advisable to incorporate the identified changes in this parameter into the adaptive control algorithm.

The presented results point to the fact that the noise of the tool as well as its temperature interacts with the thickness of the cut layer. When determining the thickness of the cut layer as an optimization parameter, both signals have the potential to be used for the identification of limit states as well as the optimization of process parameters.

The obtained results confirm the assumption that unfavorable machining conditions will be manifested by an increase in the tool temperature and the noise of the machining process. The common practice of operators, adjusting the parameters of the cut layer based on a subjective assessment of the noise level of the machining process or visual assessment and identification of thermal traces on the machined surface, is also based on this assumption.

#### **5. Conclusions**

The results of this study show that operating parameters affect both noise and tool temperature. As expected, the lowest SPL (A) = 84.7 dB was achieved at 15,000 rpm, 4 mm clearance, and a feed speed of 4 m·min−<sup>1</sup> , and the highest SPL (A) = 95.1 dB at 20,000 rpm, a radial depth of cut of 8 mm and a feed speed of 8 m·min−<sup>1</sup> . The lowest temperature of 59.7 ◦C was reached at 15,000 rpm, a 4 mm radial depth of cut, and an 8 m·min−<sup>1</sup> feed speed, and the highest temperature was 84.0 ◦C at 20,000 rpm, an 8 mm radial depth of cut, and an 8 mm feed speed. m·min−<sup>1</sup> . SPL (A) is most dependent on revolutions and interaction temperature is most sensitive to the radial depth of cut setting.

The applied methodology objectifies the given method and by its nature is suitable for application within adaptive control systems. For real use, however, it is necessary to expand the spectrum of the obtained data, not only in the range of the combination of parameters modulating the size of the cut layer but especially by the development over time resulting from the wear of the tool. A real application would help not only in the optimization of machining parameters but would also represent a suitable economy-detection tool. Based on the development of the monitored physical values, it would be possible to determine exactly the level of wear of the tool, at which it is necessary to replace it, without the need to remove the tool from the machine and procure specialized, financially demanding measuring equipment.

**Author Contributions:** Conceptualization, R.K., R.I. and M.N.; methodology, R.K., R.I. and M.N.; software, R.I. and M.N.; validation, R.K., R.I. and M.N.; formal analysis, M.G., R.I. and M.N.; resources, M.N. and R.K.; data curation, M.N. and R.I.; writing—original draft preparation, M.G.; writing—review and editing, M.G., M.N., R.I. and R.K.; visualization, M.G.; supervision, R.K.; project administration, M.N.; funding acquisition, M.N. and R.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Ministry of Education, Science, Research and Sport of the Slovak Republic, Grant Numbers VEGA 1/0324/21, Analysis of the risks of changes in the material composition and technological background on the quality of the working environment in small and medium-sized wood processing companies. VEGA 1/0714/21, Research of selected properties of sustainable insulation materials with the potential for use in wooden buildings. Slovak Research and Development Agency, Grant Number APVV-20-0403, FMA analysis of potential signals suitable for adaptive control of nesting strategies for milling wood-based agglomerates.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
