# Identification of Deformation Stage and Crack Initiation in TC11 Alloys Using Acoustic Emission
## 1 Introduction  
Aero-engines, the \"heart\" of airplanes, are large-scale power equipments, which require a high reliability to function. In an aero-engine, the low-pressure compressor blade is one of the key components and functions to complete the energy conversion [1]. It works under a harsh working environment because it is subjected to the combined effects of cycling loads during each flight cycle, including centrifugal tensile stress and centrifugal bending moment, aerodynamic stress and aerodynamic bending moment, and thermal stress and vibration alternating stress [2]. Furthermore, the blades are located at the front of the engines, making them susceptible to atmospheric corrosion [3]. As a result, the most common engine failures are caused by blade deformation and possible cracks caused by the above factors [4]. In the current maintenance protocols of aero-engines, the commonly used approaches for detecting blade deformation and cracks are the thermal infrared test method [5], ultrasonic method [6], endoscope method [7], and grating method [8]. These detection methods have to be performed with a nonworking engine, which is time consuming, labor intensive, and costly [9]. As a new type of non-destructive monitoring method, AE technology has become one of the most sought Non-Destructive Evaluation (NDE) techniques in structural health monitoring over the past few decades. In general, any external stimulus applied to the materials, such as an impact, change in pressure, or strain, would produce AE signals. It is known that AE signals contain implicit information about the material damage mechanism. These signals can be recorded by sensors and then applied to provide early warnings of the stimulus-caused damages. With the AE method, it is feasible to continuously monitor the material performance under loading. Thus, AE can be used to detect the failure of a specimen or a structure at a very early stage [10]. Given that, AE is a usefultool for structural health monitoring in situ [11]. There are many ways to recognize the different damage mechanisms from the detected AE signals. So far, most studies have employed AE descriptors such as the amplitude and energy of the signal to characterize the damage development [12; 13; 14]. The AE parameters of peak amplitude, peak energy, counts, hits, time of arrival, and rise time have also been experimentally confirmed to monitor the damage processes in composite materials and metals [10]. In the deformation monitoring of pressure vessels, AE technology was systematically studied, and relatively perfect detection standards were established [15]. [PERSON] conducted in-depth research on the physical mechanism of AE and believed that AE could become an important method in the field of damage monitoring of engineering materials due to its unique advantages over traditional non-destructive methods [10]. [PERSON] et al. pointed out that the AE signals showed different features at different deforming stages of metals [16]. Hence, if the AE signals can be correctly obtained and effectively processed, the degree of metal deformation can be characterized. Since the AE signal originates from the structural damage, the flaws can be continuously monitored for a long time after clarifying the AE signals from the damage, which is difficult to be achieved by other non-destructive testing methods [17; 18]. However, it should be noticed that the acquired AE signals are always accompanied by various noises, making the task of effectively processing the AE signals difficult. For the parameter-based AE descriptors, it is quite difficult to distinguish the AE event signals from the noise signals [10]. In the early years, the parametric analysis method was widely used for researching the AE signals due to the limitation of the available AE monitoring instruments. Based on the AE signals from aluminum and beryllium samples with defects, [PERSON] et al. found that the total counts of AE signals were directly proportional to the fourth power of the stress intensity factor of materials [19; 20].