# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 1 Introduction  
Performance degradation assessment for mechanical equipment is of importance in condition-based maintenance to lower cost, improve reliability, and reduce maintenance cost, thus, it becomes an important research area for machine fault diagnosis and reliability analysis [1; 2]. Cutting tools are one of the most important elements during machining, which have a close relationship with product precision [3]. Their reliability influences the total manufacturing effectiveness and stability of machine tools. In most circumstances, wear degradation is the main failure for tool and will lead to the waste of product and time, as well, tool condition is closely related to machine's efficiency and productivity, therefore, tool condition monitoring is important. How to estimate tool wear life is also beneficial as it is helpful for predictive maintenance. As an important concern during processing, a number of studies are being done by many researchers for tool wear. Traditional prediction and estimation approaches of tool wear are based on statistical analysis from a huge amount of historical experiment data [4], among which statistical distribution models have been used in this area, such as exponential distribution and Weibull distribution [1; 2]. This kind of method depends on plentif historical data from related equipment. Therefore, further investigation combining monitored signals and physical modes should be carried out. The key information of tool conditions can be determined from monitored data in machining. Most studies on tool wear have been carried out in tool wear compensation method for precisiontuning and providing a reference for extended tool life and good surface quality [5; 6; 7]. Some signal processing methods, such as wavelet packet decomposition and neural network, have been used to extract features from cutting force signals, AE signals, and vibration signals produced in mechanical manufacturing for tool state prediction [3; 4; 8]. Tool-wear prediction and monitoring also can improve the controlling of high-speed machining processes [9]. Most of the researches have focused on the qualitative analysis of cutting tool state, but performance degradation data of cutting tools is also meaningful in the process. EEMD is an algorithm to decompose signals, which utilizes the white noise could provide uniformly distributed scale in time-frequency space. It was mainly applied in the medical field initially. In recent years, researchers have applied EEMD to machine fault analysis and diagnosis more. The vibration signals measured from rotating machinery usually are complex, non-stationary, and nonlinear. Especially, the useful multi-fault features are too weak to be identified at the early stage [10; 11]. [PERSON] [12] combined EEMD and multiwavelet packet for optimizing mechanical fault diagnosis method. [PERSON] [13] also presented a diagnosis method for fault diagnosis of rotating machinery and improving Hilbert-Huang transform (HHT) based on EEMD and sensitive IMFs. Empirical mode decomposition (EMD) can be applied to modal analysis method [14], but there is a serious mode mixing phenomenon in EMD. EEMD, compared with EMD, has greater capacity in modal analysis for fault diagnosis and estimation. In this research, a novel method for tool wear estimation is developed by combining tool wear and monitored signals analysis based on modal analysis. Ensemble empirical mode decomposition (EEMD) for monitored signals is taken advantage of to extract the related features for tool wear analysis. An experiment data set of tool condition is used to verify the effectiveness of this method. The results show that this method can benefit to the tool wear estimation. This paper is structured as follows. The 'Algorithm Theory and Method' section introduces some algorithms of this method and the flow path in detail. The 'Experimental Study' section presents tool milling experiment, provides the data set, and analyzes the signals by using this method.