## 1 Introduction
### Current Technologies and Methodologies
The electro-tensile properties of the additively manufactured continuous carbon fiber multifunctional composites [16] and the coupled electro-flexural evaluation of 3D-printed multifunctional composites, along with their failure modes and locations, provide significant data and engineering information for aerospace applications, airworthiness, and sustainment [17]. While an automated impact damage detection methodology for the advanced solid laminate composite structures was developed based on ML classification and thermographic image processing, this paper did not adequately address the effectiveness of this methodology and the effects of the test data from the geometric variation of the tested specimens, for classification percentages and suitability in the use of ML in the design, development, manufacturing, testing and certification of aerospace vehicles [18]. The test coupons were induced to an impact damage caused by varying energy levels (4 to 12 ) [18]. A statistical analysis and ML technique were used in this study to evaluate the prediction accuracy of the proposed classification technique as ranging from 78.7% to 93.5% [18]. While the novel damage detection of advanced composites using deep neural networks (DNN), known as CNN and data fusion, yielded the gramian angular field as the best and most efficient method, this research work did not address the use of the ML in the design, development, manufacturing, testing, and certification of the aerospace vehicles [19]. Despite the advantages of the use of CNN coupled with the data fusion technique to handle the rigor and complexity of the damage states classification of the advanced composites with multi-modes of failure and mechanisms, this methodology did not address the use of the ML in the design, development, manufacturing, testing, and certification aspects of aircraft, UAS and spacecraft, utilizing the datasets from the existing databases like AGATE, NCAMP, service difficulties reporting, accidents-incidents data, design data, etc. [19]. Two ML algorithms (linear regression and random forest) were used for the prediction of the impact damaged locations on the thermoplastic aircraft control surfaces--elevators, based on the acoustic emission (AE) signals to develop a novel structural health monitoring (SHM) system for autonomous detection and localization of impact damages [20]. While the test results showed high accuracies for the random forest models and linear regression in the prediction of the impact damage locations, this study did not address the use of ML in the design, development, manufacturing, testing, certification and sustainment of aerospace vehicles [20]. A type of supervised learning algorithm known as a support vector machine was used in ML to conduct binary classification problems of the image by categorizing digital data into two groups [21]. While TensorFlow was used for target identification and movement classification, this paper did not address the use of ANN in the design, development, testing and certification of multifunctional composites for drones, spacecraft and aircraft [21]. Twelve ML techniques were used in data-driven predictions of the compressive strength of the fiber-reinforced polymer-confined concrete test coupons [22]. The experimental test results were fed into an ML model after they were processed with the new data science techniques [22]. While the tree-based methods resulted in the highest accuracy, and it was depicted that an accurate and fast computation was developed by adjusting the experimental test data and the ML models, this study did not address the use of ML in the design, development, testing, manufacturing, certification and sustainment of aerospace vehicles [22]. While the use of ML techniques in the prediction of the absorption properties of the thermoplastic composites comprised of matrix recycled polyvinyl butyral was thoroughly explored, this study did not address the use of ANN in the design, development, testing, manufacturing and certification of aircraft, UAS and spacecraft [23].