## 1 Introduction
The measured vibration and wavelet packet transform method could be effectively applied for real-time, highly accurate, and reliable roughness monitoring, with a low computation power cost in CNC machining [23]. Singular spectrum analysis was also considered as a viable strategy for assessing vibration signals used to the real-time monitoring of machined surface finish [24], as well as the calculation of surface quality in CNC turning by model-assisted response surface approach [25]. Tool vibration signals were experimentally monitored by spectral kurtosis and ICEEMDAN energy modes for insert abrasion assessment [26]. Surface quality prediction models based on a regression method and artificial neural network were developed in [27; 28]. Multiple methods involving machine learning were summarized and compared in [29]. Tool geometry was also found to be a big factor, when vibrations are assessed [30; 31; 32]. The fast Fourier transform used in this study is a valuable tool in situations when signal processing is needed. It is a computation instrument for easier signal analysis. FFT can be used on computers for power spectrum analysis as well as for filter simulation. This tool is basically an efficient way to calculate the discrete Fourier transform of data sample sequences [33] and to transform the time domain signal into frequency domain [34]. The Fourier transform was applied for surface roughness prediction to obtain the features of image texture [35], and for real-time measurement and intervention system for build-up-edge and tool damage to analyze the vibration signals for fast recognition of signal irregularities [36]. It was also successfully tested for on-the-fly CNC interpolation method [37]. In this research, fast Fourier transform was used to find dominant frequencies which were combined with noise in a composite signal. When multiple frequencies are combined, it is almost impossible to decompose a signal into original elements by signal shape assessment. After fast Fourier transform, two-sided amplitude spectrum and single-sided amplitude spectrum can be calculated with use of signal length. Other examples of the fast Fouriertransform use are the conversion of Gaussian pulse or the turning of periodical waves from the time domain to the frequency domain [38]. Experiments with MDF routing showed that a suitable cutting edge angle, with consideration of tool material, is in lower scope of ordinarily used HM tools. From the range of the research, the best performing angle was roughly 40 degrees [39]. Another factor that was both cause and effect of vibration is tool wear [2]. To mitigate this effect, many methods were tested, with a range of them focusing on MDF routing. An adaptive regulation system generating responses to advancing tool wear was developed in [40]. A neural network was tested for tool wear monitoring, where several machining factors were measured, including cutting forces, temperature, and power [41]. Mathematical models were also used for the correction of size parameters at 13 levels of a tool wear, with resulting control charts [42]. Another factor affecting vibration is clamping. Vacuum clamping systems, such as the one used in this study, are usually used for particle boards milling on woodworking machining centers. The vacuum gripper systems provide good access to the workpiece edges during machining. A downside of this securing method is un-clamped board areas with relative distance to nearest gripper. These are relatively free to vibrate in a wide frequency range while machining takes place. Due to these vibrations, the roughness of the machined edge is higher, and the process is accompanied by high acoustic emission [43]. However, all the above could be measured by other non-intrusive methods, such as vibro-acoustic analysis [44; 45; 46], or by means of energy consumption tests [47; 48]. This research focused on the evaluation of vibrations during milling of medium density fiberboards on 5-axis CNC machine.