## 1 Introduction
Similarly, [PERSON] and [PERSON] [20] used ANNs to study fatigue failures in rotary shafts. Figure 1: (**a**) A block diagram highlighting the impact of stress concentration on fatigue failure. A schematic of the (**b**) traditional machine learning and (**c**) proposed mixed learning model inspired by multilingual deep neural networks. [PERSON] et al. showed the excellence of deep neural networks (DNNs) for fatigue crack detection in notched specimens using ultrasonics [21]. Along similar lines, [PERSON] et al. [19], [PERSON] et al. [22] used ultrasonics to study damage detection. In addition to a direct application of such sensors, computational assistance in guided wave-based damage detection has also been studied with neural nets [23; 24]. However, much of the existing research has focused on damage detection of specimens having a fixed geometry, as shown in Figure 1b. It is well known that notches create localized stress concentrations that may significantly alter failure mechanisms. Since DNNs are trained with a huge amount of data, a logical follow-up question is: would a similar volume of training data be needed for any new specimen type? This question becomes even more paramount if the specimens are built with new manufacturing processes (e.g., additive manufacturing [25]) or expensive materials (e.g., nickel-based Rhenium containing superalloys [26]). In trying to find a solution to these challenges, if a DNN trained in some other material systems or geometries aids in any way, it would result in huge cost savings. Although there are transfer learning approaches to answer such problems [27], they often result in individual models for each specimen geometry and, therefore, may lead to an intractable number of trained models. While these questions are rather _firsts-of-their-kinds_ in the applied mechanics field, they are not new in other domains, such as natural language processing [28]. A common theme among the language research problems is to develop a unified natural language processing framework for sparsely available language data (such as Urdu or Tibetan) from similar yet vastly available counterparts (such as Hindi or Mandarin) using a mixed learning strategy that can be schematically represented through Figure 1c. Based on the success of this framework in translating a representative phrase from any language to the other, it is hypothesized that a single machine-learning framework can also be developed for fatigue damage detection across different specimen geometries. There can be several ways to design specimens with different geometry. In the structural engineering community, the effects of stress concentration have been studied for a long time and have led to well-defined theories for commonly occurring materials and geometries [3]. This article focuses on understanding the applications of mixed learning to specimens distinguished by stress concentration factors (Figure 1a,b). Two different stress concentration factors (\(K_{t}\)) are considered. The specimens are built from AI7075-T6, an aluminum alloy that is extensively used in aerospace applications. A custom-built fatigue testing apparatus is used to generate the required time-series data during the entire duration of the tests using ultrasonic sensors. The tests provide crack detection at a very early stage (\(\sim\)45% fatigue life) owing to the use of a high-resolution confocal microscope. Baseline deep neural nets (DNNs), trained individually for each \(K_{t}\), show above 95% accuracy. A unified DNN model is developed by mixing the data from both \(K_{t}\)s and training a single network. The unified model shows accuracies similar to the baseline DNNs, indicating the success of the unified model through the mixed learning process. To understand the impacts of the data contributions from both \(K_{t}\)s, a parametric analysis is conducted by varying the contribution from each \(K_{t}\).