# Chapter Detection in Robotic Milling Using Entropy Features
## 1 Introduction  
Industrial robots are essential for manufacturing. Robotic machining is a promising technique and attracts increasing interest [1; 2; 3; 4]. In particular, robot milling is a suitable way to deal with large parts thanks to the high dexterity and wider working space of robots. However, the rigidity of industrial serial robots is much lower than conventional machine tools [5]. Chatter is more likely to occur in the robotic milling process. When chatter occurs, the machined surface becomes rougher and the machining accuracy cannot be guaranteed [6]. Chatter has been regarded as a bottleneck that significantly limits the spread of robotic milling techniques. In the past few decades, the existing body of research has concentrated on revealing the mechanism of chatter generation during robotic milling. [PERSON] et al. [7] suggested that mode coupling chatter is an important form of chatter during robotic milling. [PERSON] et al. [8] reported that chatter is caused by the regeneration effect. However, the reasons for chatter in robotic milling can be regarded as a complicated interaction behavior containing regeneration effects and mode coupling effects [9]. Investigators have proposed several methods aimed at offline chatter suppression, such as optimizing processing parameters [10; 11] and optimizing configuration [12; 13]. Nevertheless, it is almost impossible to quantify the dynamic characteristics of a robot system precisely due to the existence of multiple flexible bodies. The results of offline chatter suppression may not be effective. Therefore, it is necessary to conduct online detection of chatter in robotic milling, and subsequently, a vibration control method can be carried out [14; 15; 16]. The issue of chatter detection in the conventional machining process has attracted a lot of interest [17; 18]. However, significant differences can be found in dynamic properties between the milling process with robots and conventional machine tools. In robotic milling, the configurations of robots can apparently influence the generation of chatter, so chatter has time-varying characteristics along a machining trajectory. Existing studies in the fieldof chatter detection mainly focus on milling with conventional machine tools, which may be not applicable for chatter detection in robotic milling. Until now, far too little attention has been paid to chatter detection in the robotic machining process. [PERSON] et al. [19] developed a novel approach for real-time chatter monitoring using discrete wavelet transform. The chatter fault threshold was determined based on the standard deviation of the wavelet signals. [PERSON] et al. [20] proposed a new method for chatter detection in robotic drilling combining the notch filter and local maximum synchrosqueezing transform (LMSST). [PERSON] et al. [21] used entropy features extracted from adaptive variational mode decomposition (VMD) for chatter detection in robotic milling. [PERSON] et al. [22] proposed a chatter detection model based on VMD, information entropy, and SVM. However, there are some deficiencies in the existing works. Firstly, fast and reliable detection of chatter still remains a challenge. Secondly, fixed thresholds of chatter indicators are commonly set for detecting chatter [19; 23; 24; 25; 26]. In practice, cutting parameters and robot configurations may be different with the change in machining conditions. The applicability of using a manual setting threshold for variable machining conditions requires further validation. Taking into consideration the time-varying characteristics of chatter, time-frequency analysis methods show advantages in online chatter detection. Typical time-frequency domain analysis methods, such as VMD methods [21; 27], suffer the complicated process of parameter optimization. Ensemble empirical mode decomposition (EEMD) [25; 28; 29] has the shortcoming of residual noise and difficulty in averaging operations with different numbers of IMFs.