## 1 Introduction
### Current Technologies and Methodologies
While a novel method called Lightweight Trustworthy Message Exchange (LTME) in Unmanned Aerial Vehicle Networks was proposed to potentially offer improved functionality and better robustness with a low computation overhead, this research does not address the use of ANN in design, development, additive manufacturing and certification of multifunctional composites for drones, aircraft and spacecraft with respect to how the aerospace structures and airframes are designed, developed, additively manufactured, tested and certified [30]. The latest journal article about the use of artificial intelligence in aviation does not address the use of ANN in design, development, additive manufacturing, testing and certification of multifunctional composites for aerospace vehicles [31]. A new taxonomy atlas of composite materials' damage patterns, automated damage diagnostics, analysis methods and measuring signals was developed [32]. While the supervised and unsupervised ML algorithms like self-organizing maps, CNN, autoencoders and a novel z-profiling methodology were used for an assessment, this investigation did not address the use of ANN in the design, development, manufacturing, testing and certification of aircraft, UAS and spacecraft utilizing the common database(s) such as AGATE, NCAMP, etc. [32]. The failure modes, failure locations and failure orientations of the tested specimens play significant roles in the aerospace application and airworthiness state, and are also vital for maintaining the continuous operational safety (COS) known as sustainment. The lack of existing standards and the maturation of technologies make it difficult to certify AI-enabled and additively manufactured multifunctional carbon fiber composites for aircraft. This research's main objective is to explore the use of ANN in the design, development, additive manufacturing and certification of multifunctional composites for aircraft, UAS and spacecraft. The current state of technologies does not address the use of ANN in the design, development, additive manufacturing, testing and certification of multifunctional composites for aircraft. Rapidly increasing computing technologies like ANN in advanced manufacturing have explored multifunctional composites. Accuracy, precision, refinement, etc., play vital roles in maintaining the repeatability, quality and variability of the AI-enabled AM multifunctional composites. The Advanced General Aviation Transport Experiments (AGATE) and National Center for Advanced Materials Performance (NCAMP) programs that were built for sharing databases furnished by material suppliers and reducing costs in airworthiness certification need to implement the use of ANN for addressing the shortcomings of the existing procedures and processes. The materials and processes are built and accepted with respect to specific aircraft models and could not be used for other aircraft models per the existing framework and procedures. This allowed using the material and processes' information from this shared database when the same material and processes are used in similar aircraft applications with proper substantiation. The data from the current reporting systems, such as the Service Difficulty Reporting System (SDRS), U.S. National Transportation Safety Board (NSTB), National Aeronautics and Space Administration (NASA), U.S. Department of Defense (DOD), Airlines, etc., is not fully utilized to the maximum potential in order to build ANN architecture and predict failure loads and failure modes. In this paper, Section 2 covers the proposed methodology highlighting the shortcomings of the existing procedures and framework and focuses in detail on the great advantages of the proposed solution and methodology. Section 3 addresses the machine learning model for structural characterization, and Section 4 addresses the conclusion and future work opportunities. Section 3 identifies the mathematical approach for machine learning and evaluation metrics for the machine learning validation in Sections 3.1 and 3.2, respectively.