## 1 Introduction
### Current Technologies and Methodologies  
An automated inspection system based on convolutional neural networks (CNN) and image segmentation tasks was proposed to enable rapid part inspection and analysis [7]. An ML approach for the planning, optimizing, and inspection of AFP processes was proposed to automate data capturing, data storing, modeling, and optimizing [8]. Deep learning is quite popular and seeks to extract the representative features of inputs in order to identify unique patterns in the data [9]. CNNs are deep learning algorithms that can train large datasets with millions of parameters in the form of 2D images as input and convolve them with needed filters to produce the desired outputs by using convolution [10]. The CNN models were created to assess their performance in image detection and recognition from the assigned datasets [10]. Recurrent neural networks (RNNs) are defined as a class of neural networks in which the previous outputs are used as inputs while having hidden states [11]. Despite the various benefits of using RNNs (processing input of any length; model size not increasing with the size of input; computation considers historical information; weights are shared across time), the computation is slow, which makes it difficult for gathering information from a distant historical event, and it cannot consider any future input for the current state [11]. RNNs are used for applications that contain data in a sequence and time series for classification purposes [12]. While tailoring the electrical and structural properties of 3D-printed composites and the quasi-static multifunctional characterization of 3D-printed carbon fiber composites is the critical step for their compressive-electrical properties, this paper did not address the use of ANN in design, development, additive manufacturing, and certification of the multifunctional carbon fiber composites for aircraft, drones, and spacecraft [13]. The reported quasi-static multifunctional properties and the failure modes and locations, coupled with machine learning (ML), could help inform future iterations of the design, development, and advanced manufacturing with the hope of offering significant weight reductions and potentially replacing the bulky electrical wires in aerospace applications [13]. A novel automated quantification methodology was developed to evaluate the health and performance of the wind turbine blade leading edge erosion using the data from the 140 field images covering the varying blade orientation, resolution, aspect ratio, and lighting [14]. Supervised ML and unsupervised ML models were created for evaluating the automated quantification of the damage states obtained from the field images of the wind turbine leading edges. While the supervised ML model applied convolutional neural networks (CNN) and learnt damage details/types as features that are found in the typical datasets of training of the 140 field images, the unsupervised ML model conducted a consolidation of pixel intensity thresholding with the calculation of a pixel-by-pixel shadow ratio to independently identify features within the 140 field images of the wind turbine leading edges [14]. It was found that the CNN model was better in the identification of shallow damage and yielded higher performance when used with the 140 field images after their preprocessing to common blade orientation [14]. While there have been various damage classification techniques using the supervised ML models for self-organizing maps in the structural health monitoring (SHM) systems, this study did not address the use of ML in the design, development, manufacturing, testing, and certification of the aircraft, UAS, and spacecraft using the digital data from across different entities such as accidents/incidents databases, service difficulty reporting, airlines, maintenance, repair and alteration, design-manufacturing-testing data, etc. [15].