## 2 Proposed Methodology
The use of the common material database(s) that are needed for the design, development, manufacturing, testing, certification, qualification, equivalency and sustainment of aerospace vehicles is vital for the integrity and continued operational safety (COS) of aerospace vehicles. A number of companies can benefit from using the same data from this common database(s) without going through the complete and extensive manufacturing and testing of advanced composite structures. The use of ANN in the design, development, manufacturing, testing and certification of advanced composites for aerospace uses on the common material systems databases, by applying supervised machine learning via com  
Figure 1: Damage growth mechanism and final failure mode observed in damaged sandwich specimens [34]. puter vision technologies to compare the data with respect to the existing databases such as accident-incidents data, service difficulties reporting, design-manufacturing-testing data, etc., makes this novel methodology a promising technology to advance safety, integrity and the extended life of an aircraft, UAS and spacecraft. Figure 3: Final failure modes observed in sandwich panels for undamaged, damaged and repaired configurations (redrawn [35]). Figure 2: Different failure modes of specimens with varying numbers of holes (redrawn [35]). Figure 6 shows the proposed functional architecture for the use of ANN in the design, development, additive manufacturing, testing and certification of multifunctional composites for spacecraft, aircraft and UAS. As shown in Figure 6, the centralized material database(s) can be utilized and accessed by any of the numerous companies (Company A through Company nth) for a set of different material systems (Material-1 through Material mth) to be used in the aircraft, UAS and spacecraft projects. The use of ANN in the design, development, additive manufacturing and certification of multifunctional composites for spacecraft, aircraft and UAS, as shown in Figure 6, makes it more robust and comprehensive, thereby resulting in a digital data-centric assessment of the advanced composite structures for their suitability in aerospace applications as well as the continued operational safety (COS) of aerospace vehicles. This proposed methodology is applicable to both in situ and off-site manufacturing. The use of the phrase \"in-situ manufacturing\" in this context is meant for the integrated design-manufacturing-testing technologies, in which the design, manufacturing and testing of the advanced composite structures take place at the same machine or equipment/tool with the help of the ANN. The use of ML, especially supervised machine learning, in the design, development, additive manufacturing and certification of multifunctional composites for spacecraft, aircraft and UAS makes it more streamlined compared with the traditional methodologies for aerospace vehicles. The operational, maintenance, inspection, accidents, incidents, service difficulties and other data from certification, qualification and equivalency, coupled with the supervised machine learning algorithm, makes this proposed methodology best suitable for the design,  
Figure 4: Typical tensile test failure modes on solid laminate polymer composites (Reprinted, with permission, from ASTM D3039/D3039M-00, Standard Test Method for Tensile Properties of Polymer Matrix Composite Materials, copyright ASTM International. A copy of the complete standard may be obtained from www.astm.org (accessed on 10 December 2023)) [36]. development, additive manufacturing and certification of multifunctional composites for UAS, spacecraft and aircraft. The proposed methodology, as shown in Figures 5 and 6, offers potential improvements and benefits to the overall safety, integrity and life of an aircraft, UAS and spacecraft due to the use of ANN in the design, development, additive manufacturing and testing, as well as the certification of multifunctional composites for spacecraft, UAS and aircraft.