Materials of Unmanned Aerial Vehicles (UAVs) parts require specific techniques and processes to provide high standard quality, sufficiently strong, and lightweight materials. Composite materials with a proper technique have been considered to improve the performance of UAVs. Usually, the hybrid composite is developed by mechanical properties with the addition of the filler component (i.e., particle) in a matrix. This research work aims to develop the effective composite materials with better mechanical properties. Considering the manufacturing of hybrid composite materials, the vacuum process is an affecting factor on mechanical properties. The comparison of the hand lay-up process (HL) and vacuum infusion process (VI) with controlled pressure and temperature are studied in this research. In addition, graphite fillers (i.e., 5 wt%, 7.5 wt%, 10 wt%, and 12.5 wt%) are added to the studied matrix. Obviously, the ply orientation is one of the factors that affects mechanical properties. Moreover, two types of ply orientation (i.e., [0°/90°]4s and [−45°/45°]4s) are comprehensively investigated to improve mechanical properties in the three-point bending test. The experimental results show that the vacuum infusion process of ply orientation [0°/90°]4s with the addition of 10 wt% graphite filler exhibits remarkable flexural strength from 404 MPa (without filler) to 529 MPa (10 wt% filler). Especially, the ply orientation of [0°/90°]4s has higher flexural strength than [−45°/45°]4s in both processes. Considering the failure, the fracture of the specimen propagates along the trajectory of fiber fabric orientation, leading to the breakage. Subsequently, the flexural strength under the vacuum infusion process is more significant than in the hand lay-up process. Effectively, it is found that the hybrid composite in this manufacturing has a higher strength-to-weight ratio to use in the structure of UAV instead of pure aluminum. It should be noted that the proposed hybrid composite strategy used in this study is not only limited to the UAV parts. The contribution can be extended to use in other applications such as automotive, structural building, and so on.
There is a developing demand for natural resources because of the growing population. Alternative materials have been developed to address these shortages, concentrating on characteristics such as durability and lightness. By researching composite materials, natural materials can be replaced. It is vital to consider the mechanical properties of composite materials when selecting them for a specific application. This study aims to measure the flexural strength of carbon fiber/epoxy composites. However, the cost of forming these composites is relatively high, given the expense of composite materials. Consequently, this study seeks to reduce molding costs by predicting flexural strength. Conducting many tests for each case is costly; therefore, it is necessary to discover an economical method. To accomplish this, the flexural strength of carbon fiber/epoxy composites was investigated using an artificial neural network (ANN) technique to reduce the expense of material testing. The output parameter investigated was flexural strength, while input parameters included ply orientation, manufacturing, width, thickness, and graphite filler percentage. The scope alternative was determined by identifying the values of variables that substantially affect the flexural strength. The prediction of flexural strength was deemed acceptable if the mean squared error (MSE) value was less than 0.001, and the coefficient of determination (R2) was greater than or equal to 0.95. The obtained results demonstrated an MSE of 0.003039 and an R2 value of 0.95274, indicating a low prediction error and high prediction accuracy for all flexural strength data. Thus, the outcomes of this study provide accurate predictions of flexural strength in the composite materials.
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