# Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review
## 3 Experimental Techniques
### On-Line Chatter Classification, Detection, and Monitoring  
Before it is fully evolved, chatter identification at early stages is crucial for its suppression or minimization in real-time applications. For this purpose, the time-efficiency method for monitoring of vibration or/and process signals is a key issue to be embedded in CNC controllers and other external devices. Several techniques have been used for chatter recognition based on pattern recognition, for instance via support vector machine [88], sensor-less based on indexes of power-factor theory [89], topological data analysis, or the use of regression neural networks where non-linear effects need to be faced [90]. [PERSON] et al. [89] proposed a sensor-less chatter detection method. To do so, they used a mechanical energy factor (MEF) and a mechanical power factor (MPF) which are useful when it comes tracking unstable cutting. These are indicators of the phase difference between the dynamic cutting force and velocity-displacement. From experimental tests in a precision lathe, the authors were capable of detecting chatter vibration under small number of computations. Later on, [PERSON] et al. [91] combined supervised machine learning with Topological Data Analysis (TDA) to obtain an indicator of chatter imminent presence. Under this approach, deterministic and stochastic turning models (with varying cutting coefficients) work together. [PERSON] [92] studied a tridimensional turning process by using neural network approach. Their model proved to represent nonlinear effects better than conventional time series models. Besides, the accuracy was better at higher cutting speeds, where the spacing between lobes is higher. [PERSON] et al. [93] applied and studied the behavior of applying an artificial neural network (ANN) when it comes to modelling stability in turning. The stability boundaries were used as a starting point for the creation of datasets to train the ANN. They found that the ANN was successful in predicting stability at >90% of the cases. [PERSON] and [PERSON] [94] analyzed the relationship between cutting parameters and chatter degree using mathematical model of responses based on response surface methodology (RSM) and artificial neural network (ANN). The applied the Wavelet Transform to remove noise from the raw signals and showed that ANN was more reliable than RSM. To detect chatter severity, they obtained a chatter index. [PERSON] et al. [95] used a similar approach, they applied the wavelet transformation to denoise the raw signal, identification of chatter frequency, and calculation of chatter index. More recently, [PERSON] and [PERSON] [96] proposed an operational modal analysis to predict chatter onset in turning processes. In this study, the authors used a stability margin of the process thus being able to predict chatter onset before the vibrations are unacceptable. Aspects such as uncertainty or the relative randomness of process parameters was studied in some works. For instance, [PERSON] et al. [97] calculated the reliability probability of chatter in a turning system. They used the first order second moment method (FOSM) and fourth moment method and compared with Monte Carlo simulation presenting a modified version of traditional stability lobes. For the experimental validation, they programmed increasing depths of cut to cross the stability boundary limits. Similarly, [PERSON] et al. [98] obtained the width of cut to spindle speed ratio using Laplace  
Figure 5: (a) Stability lobes in turning with constant spindle speed and spindle speed variation (SSV) technique [85]; (b) optimum relationship between RVF (dimensionless spindle speed modulation frequency) and RVA (dimensionless spindle speed modulation amplitude) [86]. transform making use of the Monte Carlo method and the advanced first order second moment method. They verified predictions with practical examples. [PERSON] et al. [99] applied the Linear Mixed Model (LMM) for chatter prediction as well as for wear prediction with a good accuracy.