Acoustic Emission of Metallic Specimen with Surface Defect During Fatigue Crack Growth

Abstract

Acoustic emission is defined as the phenomena whereby transient elastic waves are generated by the rapid release of localized sources within a material. During fatigue crack growth, the formation of new crack surfaces is associated with a sudden release of energy, which constitutes acoustic sources for acoustic emission. This paper investigates the acoustic emission signature arising from fatigue test of a metallic specimen under tensile fatigue test. In this experimental study, dog-bone aluminium alloy specimen with a surface defect was fatigued to failure. It is found that the acoustic emission characteristics are different during the propagation of surface crack, because the source is changing. The results provide a useful guide in identifying source origin based on the characteristics of the acoustic emission waveform. Introduction Aluminium alloys have been used in aerospace since the 1920s. The strength, hardness, and corrosion resistance of aluminium alloys increased dramatically as the aerospace industry developed. Although composite materials have been employed over recent decades, aluminium alloys are still widely used and contribute to over 50% of the total weight of an aircraft [1]. It is known that aluminium alloys are susceptible to fatigue damage and there has been a significant amount of research towards the development of structural health monitoring methodologies to detect and monitor the onset of fatigue damage and the eventual fatigue crack growth [2-5]. The advanced non-destructive testing methods for metal material include near-infrared cameras (NIR), laser ultrasonics, and X-ray computed tomography [6-8]. Although these methods can reveal the defect by image clearly, there is still a need for a non-destructive method which has the potential to reveal the fatigue failure stages. In-situ acoustic emission (AE) testing is a widely used non-destructive testing approach for locating and classifying defects in rock, concrete, composite, and metal [9-14]. It is a real-time monitoring method of efficiency and high-performance and is used in monitoring equipment under active stress or machining processes [15-17]. According to previous research, AE has the potential to predict the stages of crack propagation during operation [4, 5, 18-20]. Thus, the AE method has a real and significant application in real-time structural health monitoring. When using AE to monitor fatigue crack growth, the possible AE sources include the formation of a new fracture surface and the rubbing or clapping of interface crack surface [21, 22]. Structural Health Monitoring Materials Research Forum LLC Materials Research Proceedings 18 (2021) 95-104 https://doi.org/10.21741/9781644901311-12 96 The transient wave in an AE signal is called hit, and it is usually understood as “an isolated and separated” waveform [23]. Previous studies on AE were focusing on hit-related information during fatigue tests, including the relationship between count rate and material rolling direction [24], and the relationship between count number on fatigue cycle and crack propagation rate [5]. Nevertheless, recent works on AE have focused on waveform pattern analysis and highlighted the importance of studying AE waveform [25, 26], because it could provide more information than only analysing hit-related characteristics. It is found that AE signals of aluminium alloys 7075-T6 have peak frequencies around 100 kHz, 260 kHz and 600 kHz [20]. Other research also found different frequency peaks of low carbon steel [26] or aluminium alloy 2024 T3 [27]. Studies on AE waveforms are often challenging because the amplitude and frequency of AE waveforms can vary significantly. Sause and Hamstad stated that the frequency characteristics could be relatively irrelevant in some cases, because the AE sensors were of variety and could affect collected frequency [28]. For example, some sensors may provide more information on frequency than others [25]. Therefore, it is important to have a more comprehensive rule to cluster AE hits, so wave modes identification becomes very important. The elastic waves excited by AE signal in a thin plate-like structure are Lamb waves, which have symmetric modes and asymmetric modes. The most frequently discussed modes include zero-order asymmetric mode (A0), zero-order symmetric mode (S0), and first-order asymmetric mode (A1). The group velocities for distinct wave modes vary with frequency due to its dispersive nature and can be used in identifying wave modes. The most frequently used AE sensors are piezoelectric wafer active sensors and typically are threshold-based sensors that only record AE signals which exceed the set threshold level to discard possible background noise [26]. Unlike the active wave sensing approach, where guided waves are excited by a transmitter sensor [29], AE is a passive monitoring approach that listens for waves generated by rapid energy released from the structures themselves. Making use of this phenomenon, by aligning the theoretical dispersion curve with the wavelet transform, certain wave modes can be identified [30]. The influence of the source location in the thickness direction to AE wave modes has been investigated by some researchers for plate-like structure. Hamstad [31] showed that the PLB source location in the thickness direction of plate affects the generated wave modes: the in-plane PLB signal near top of the edge has only A0 mode, and that near mid-plane has both A0 and S0 modes. Yu et al. [32] differentiated the delaminations and transverse cracks of composites by A0/S0 mode amplitude ratio. They also conducted an FEA of a thin plate with monopole input source, and results indicated that as the source location moved closer to the surface from the mid-plane, the ratio of A0 and A1 modes increased, and that of S0 decreased. As a result, wave mode decomposition results have the ability to indicate source locations and source origins. This paper presents a set of findings in using AE to monitor the fatigue crack development in an aluminium plate-like specimen. In describing the formation of new fatigue crack surface as a source of acoustic emission, the paper reports on the differences in the stress wave generated by propagating radial crack front. Methods Experimental method. The samples tested were dog-bone specimens with gauge width 40mm of and thickness of 3mm (Fig. 1a) made from aluminium alloy 6060 (Al6060) and its material properties are shown in Table 1. It has a thin surface defect (2mm length, around 0.5mm width, and 0.5mm depth) in the centre of the specimen (shown in Fig. 1b). The defect was first drilled with a 0.5 mm diameter drill for 0.5 mm depth in the middle, and then a 2 mm length 0.5 mm Structural Health Monitoring Materials Research Forum LLC Materials Research Proceedings 18 (2021) 95-104 https://doi.org/10.21741/9781644901311-12 97 depth line crack was engraved over it. The artificial defect was intended to cause stress concentration and initialise the fatigue fracture. Table 1: Al6060 specimen with through-thickness defect material properties. Young’s Modulus 68 GPa Poisson’s Ratio 0.33 Density 2.7 g/cm3 Tested yield stress 100 MPa Tested ultimate stress 150 MPa