**1. Introduction**

Current aircraft structural design and qualification methodologies require large amounts of testing in bottom-up type approaches that typically start at the coupon level and extend to full aircraft evaluation [1–7]. The prescribed assessment process is, therefore costly and time consuming, which additionally makes the adoption of new materials or design modifications difficult. This is especially challenging in the case of composite materials for which slight changes in manufacturing parameters can invalidate prior test data and require re-qualification of the material performance [8,9]. Typically, once extensive coupon testing is completed, design limit loads are computed for specific critical components. In this process, safety factors are added to account for reliability and uncertainty

effects. Furthermore, pass/fail qualification criteria are implemented during component testing. This approach, however, limits the information that engineers can gather during full-scale part qualification testing that can be related to material performance.

In an effort to gather supplemental data, prior research has explored nondestructive evaluation (NDE) methods, including acoustic emission (AE), thermography, shearography, X-ray computed tomography (XCT), and ultrasonic testing (UT), for use during composite end-item qualification testing [10,11]. NDE data can, in general, augment existing structural test protocols by providing additional datasets, while also offering insight into damage initiation and progression. Modern NDE tools, however, have not yet been fully integrated into legacy aircraft qualification testing protocols, partially due to a lack of expertise, equipment, and knowledge on the usefulness of such methods, or even confidence that NDE could assist in this process. In this context, the U.S. Federal Aviation Administration has only recently proposed that the use of NDE tools for on-board structural health monitoring (SHM) could provide more confidence in complex aircraft designs while decreasing risk and maintenance costs [12].

AE is a NDE tool with the immediate potential to augment existing airframe strength test protocols, which is the focus of this investigation. Specifically, AE is a versatile, passive NDE method that senses pressure waves emitted from a variety of sources, predominantly linked to damage, across length scales and materials [13–16]. A physical analogue for AE is seismic activity resulting from tectonic plate motion of the Earth's crust. Provided that the appropriate sensing equipment and data-processing is applied, AE can provide real time volumetric information about dynamic changes related to damage. Based on this method, material damage processes at the coupon [17–20], component [21–24], and structural [25–27] scales have been reported. AE datasets can be further leveraged for advanced analytics such as data-driven models and machine learning [28–32], which may lead to a validated SHM methodology with potential even to be applied on-board, e.g., on aircraft [33].

Structural monitoring using AE is most commonly found in civil infrastructure [34–37], where it can be employed to passively collect data while a structure remains in use. The AE method has also been applied in wind turbine applications to prevent catastrophic failure of the large composite blades [38–41]. In addition, there have been some attempts to use AE in aircraft structural monitoring and component testing. Both military and civilian aircraft examples have been reported [42]. Other aircraft-focused investigations have used AE to examine damage localization and ultimate failure in landing gear [43], assess skin-spar bond-line integrity [44], evaluate impact damage locations [45,46], assess damage modes in composite fuselage under complex loading [47], determine the failure modes in composite airframe parts under quasi-static and cyclic loading [48], and assess rotorcraft composite fuselage durability and damage tolerance [49]. However, more progress is needed to gain the confidence required to integrate AE into structural qualification test standards [50,51].

In general, AE analysis and post-processing can leverage the recorded waveforms and a number of useful parameters extracted from them. Common AE waveform parameters are described in Shull [13] and ASNT's NDT Handbook [52]. Certain AE patterns can provide information about the test specimen's structural integrity, beyond pass/fail load criteria. For example, AE activity may not be observed until load levels are reached that previously induced damage. Specifically, if a structure is progressively loaded, new damage may not be initiated until the prior maximum load has been exceeded. This observation is known as the Kaiser effect [13,53], which, however, may not hold for composite materials similar to those evaluated in this manuscript [54,55]. In cases where AE activity is detected at loads that are lower than the previous maximum, then a specific number is computed called the Felicity ratio (FR)—defined as the load at which significant AE reinitiates, divided by the previous maximum load. Materials that consistently emit AE at loads below their previous maximum, thus, have a FR value of less than one, and investigations have shown that they exhibit progressive damage behavior. In fact, changes in observed Kaiser and FR effects as well as other AE activity patterns have been correlated with the presence of damage [56–59].

Based on this introduction, this research seeks to evaluate the progressive failure of full-scale, composite aircraft spars using AE during structural testing, and associate such information with ultimate failure prognosis. The insight gained from AE is intended to augment current test methodologies in order to capitalize on readily-available data and decrease the time taken to qualify novel airframe materials. The overall approach described includes the following parts: (1) characterization of damage progression by examining AE activity trends; (2) identification of probable damage regions; (3) ultimate failure load prognosis; and (4) statistical evaluation of the spar static strength requirements using AE data.
