Generative design and structural optimization of a hexacopter frame for agricultural UAV spraying

 Corresponding author: Yamral Kassanew Akele Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Generative design and structural optimization of a hexacopter frame for agricultural UAV spraying Yamral Kassanew Akele 1, * and Belaynesh Kassanew Akele 2 1 Graduate Institute of Intelligent Manufacturing Technology, National Taiwan University of Science and Technology, No. 43, Keelung Rd, Sec.4, Da’an Dist., Taipei 10607, Taiwan. 2 Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, No. 43, Keelung Rd, Sec.4, Da’an Dist., Taipei 10607, Taiwan. Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-086 Publication history: Received on 02 June 2025; revised on 08 July 2025; accepted on 11 July 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.24.1.0217 Abstract This study presents a generative design-based optimization of a hexacopter drone frame intended for agricultural spraying applications. Utilizing artificial intelligence-driven algorithms within Autodesk Fusion 360, the research explores multiple frame configurations based on structural constraints and performance objectives. Finite Element Method (FEM) simulations, conducted through both ANSYS and Fusion 360, modeled dynamic operational scenarios such as landing impacts and aerial collisions. These simulations enabled the identification of overstressed regions, leading to iterative material removal and geometric refinement. The finalized frame design achieved a mass of 2.784 kg, translating to a 7.2% to 38.1% reduction compared to traditional 10-liter agricultural drone frames, while maintaining a high safety factor range of 9.60 to 15 and a maximum displacement of just 1.288 mm. Furthermore, flight performance analysis indicated a linear reduction in throttle requirement from 80% to 32.6% as tank weight decreased from 16.88 kg to 6.88 kg, thereby extending flight duration from 6 to 14.6 minutes. These results demonstrate the effectiveness of generative design for developing structurally efficient and operationally robust UAV frames. Future research will explore foldable design strategies to further enhance portability and field adaptability. Keywords: Agricultural drone; Finite Element Method; Flight performance analysis; Generative design; Structural optimization; UAV frame design 1. Introduction In recent years, the agricultural sector has increasingly embraced drone technology to address the pressing need for precision, efficiency, and cost-effectiveness in farming operations. Drones, also known as Unmanned Aerial Vehicles (UAVs), have been widely recognized for their ability to perform diverse agricultural tasks such as crop monitoring, pesticide spraying, and field mapping. These systems offer improved data accuracy, reduced operational costs, and enhanced crop productivity, positioning them as key enablers of modern precision agriculture [1] and [2]. Historically, Japan was a pioneer in using drones for agricultural spraying applications as early as the 1980s, leading to a broader global interest in integrating UAVs into agricultural workflows [3]. The economic impact of drone usage in agriculture is significant. Studies have shown that drone integration can lead to annual cost savings of up to $1.3 billion for wheat and soybean farmers in the U.S., with yield increases between 2.5% and 3.3%, and ROI reaching as high as 146% in some regions [4] and [5]. Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 68 Despite these benefits, commercially available drone frames often fall short in terms of weight efficiency, structural resilience, and adaptability to specific agricultural tasks. Off-the-shelf designs tend to lack customization, resulting in excess weight that diminishes flight endurance and payload capacity. Moreover, they are rarely optimized to withstand high-frequency vibrations from spinning rotors or absorb impact forces from landings, both of which are critical for maintaining UAV reliability in rugged farming environments [6]. Conventional manufacturing techniques impose geometric constraints on drone frame design, which often limits performance optimization. These techniques can result in over-engineered parts with redundant materials, increasing overall mass and decreasing aerodynamic efficiency [7]. Addressing this challenge requires design methodologies that not only meet structural demands but also facilitate weight minimization without compromising mechanical robustness. Lightweight frames reduce energy consumption, prolong flight duration, and improve payload capacity, all of which are critical parameters for efficient agricultural UAV operation [8]. Generative design a computational approach that iteratively explores optimized geometries within specified constraints has emerged as a powerful tool for producing lightweight yet structurally robust drone components. This method, particularly when combined with additive manufacturing (3D printing), enables engineers to create drone frames that are tailored to specific use cases, balancing performance and manufacturability [9] , [10]and [11]. Additive manufacturing further enhances the design process by allowing the fabrication of complex topologies that would be difficult or impossible to produce using conventional manufacturing methods. Structural performance analysis is equally vital in ensuring that UAV frames endure the mechanical stresses encountered during operation. Finite Element Method (FEM)-based simulation tools such as ANSYS and Fusion 360 enable accurate evaluation of stress distributions, modal behavior, deformation, and fatigue life under various loading conditions. These simulations are instrumental in identifying overstressed regions and guiding material distribution strategies that ensure safety and reliability [12]. The integration of drones into agricultural operations has prompted significant research on structural optimization and UAV design. Studies have investigated material selection, geometric configurations, and topology optimization strategies to improve drone performance. analyzed various lightweight structural materials for UAV frames, noting that carbon-fiber-reinforced composites offer an ideal balance of strength and weight for aerial applications. Similarly, Sone and Kaneko (2021) explored the impact of vibration-resilient frame designs on drone stability during pesticide spraying. Generative design has been increasingly applied in drone and aerospace applications for producing lightweight components with tailored mechanical properties. Applied generative design methods in Autodesk Fusion 360 to minimize mass in drone arm structures while maintaining deflection constraints [13]. Their results revealed up to 40% mass reduction compared to traditional CAD designs, with improved stiffness-to-weight ratios. Likewise, demonstrated that generative designs for UAV brackets outperformed manually designed counterparts in modal analysis, achieving higher natural frequencies and reduced resonance risks [14]. In agricultural contexts, frame design must accommodate payload variability, environmental exposure, and terrain-induced landing shocks. reviewed FEM analyses showing that stress concentrates around rotor hubs and payload mounts during rough landings supporting the need for targeted reinforcement zones as reviewed by [15]. Similarly, FEM simulations on a 30 kg hexacopter and proposed reinforcement strategies to resiliently absorb impact forces during field landings, ensuring critical load-bearing regions maintain structural integrity as conducted by [16]. 3D printing and additive manufacturing technologies have also facilitated the realization of generatively designed UAV structures. A hexacopter prototype constructed using fused deposition modeling (FDM) with PLA and fiber-reinforced PA12 successfully met structural requirements [8], while reducing total weight significantly compared to conventional aluminum frames [17]. Despite these advancements, limited studies have holistically integrated generative design with structural simulation and additive manufacturing in agricultural drone development. Most prior works focus on either material optimization, modal analysis, or topology refinement in isolation. This study addresses that gap by combining generative design with FEM analysis and additive fabrication in the development of a structurally efficient, application-specific hexacopter frame. This study proposes the use of generative design techniques to develop a hexacopter drone frame specifically tailored for agricultural applications. The frame will be fabricated using 3D printing to leverage the design freedom of additive Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 69 manufacturing. Particular attention is given to optimizing structural integrity under two key mechanical conditions: fatigue loading induced by continuous rotor vibration, and sudden impact from landings or collisions. The study aims to demonstrate that combining generative design with additive manufacturing can produce a structurally efficient, lightweight, and application-specific drone frame capable of withstanding operational demands in precision agriculture. 2. Methodology This study employed a structured methodology that integrates generative design principles, simulation-based structural analysis, and design validation, specifically tailored for agricultural drone applications. The primary objective was to develop an optimized hexacopter frame capable of withstanding field-induced dynamic and impact loads while minimizing structural weight through advanced design strategies. The overall methodological framework illustrated in Figure 1 systematically connects UAV requirements, design exploration, simulation, and performance evaluation. Each stage was iteratively refined based on simulation feedback and real-world constraints. The use of Autodesk Fusion 360 enabled seamless transition from conceptual modeling to performance-based optimization. Together, these steps ensured a balance between manufacturability, mechanical robustness, and mission-specific efficiency. This process is organized into five core stages, as described below. • Product Specification and Requirements Definition: A literature review informed the selection of structural materials, geometry, and design constraints based on agricultural UAV needs—such as payload capacity, tank size, and flight endurance. These parameters defined the design envelope for additive manufacturing. • Generative Design and Simulation: Autodesk Fusion 360 was used to apply generative design techniques using PLA and ABS. PLA was preferred for its higher stiffness. The tool generated multiple lightweight configurations, and the most structurally efficient design was selected for further analysis. • Fatigue Analysis: Cyclic motor vibrations were simulated using ANSYS to evaluate fatigue life through S-N curves and the Goodman criterion. Stress concentrations were primarily observed at the motor mount junctions, yet the results confirmed long-term operational reliability. The analysis showed that the material endurance limits for ABS were not surpassed, ensuring resistance to crack initiation and propagation under fluctuating loads. These results validate the drone frame’s durability under repeated flight cycles and support its suitability for sustained agricultural missions. • Impact Resistance Analysis: Transient simulations modeled sudden landing and collision scenarios. Von Mises stress and deformation results demonstrated that the frame could withstand impact without structural failure. • Validation and Manufacturing Consideration: The final design was validated for both performance and printability using FDM. Slicing simulations confirmed feasible fabrication, and the PLA prototype was deemed suitable for production. Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 70 Figure 1 Methodology flow diagram for drone frame development and optimization 3. Concept Design 3.1. Conceptual Design and Geometry Selection The agricultural drone frame was conceptually designed to balance lightweight structure, mechanical strength, and functional performance. Generative design software explored structural variations such as arm thickness, crosssections, and internal lattice geometries to reduce material use without sacrificing integrity [10]. 3D modeling and Finite Element Analysis (FEA) were used in tandem to assess the mechanical behavior of each candidate design under simulated flight conditions. This iterative process ensured alignment between structural performance and manufacturing constraints, producing an efficient and field-ready frame. Key performance factors fatigue resistance, impact absorption, and weight efficiency guided the configuration. Fatigue simulations modeled cyclic rotor-induced stresses, while impact tests evaluated the frame’s ability to handle landing shocks and vibrations. Generative design helped optimize the frame’s stiffness-to-weight ratio. From these insights, technical requirements were defined to ensure payload capacity, 3D printability, emergency resilience, durability through repeated use, and manufacturability. Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 71 Figure 2 Quadcopter (a), hexacopter (b), Octocopter (c), and tricopter (d) The drone frame’s geometry was critically selected to optimize aerodynamic efficiency, payload support, and system stability. Various configurations quadcopter, hexacopter, octocopter, tricopter, and fixed-wing—were analyzed. Quadcopters (Figure 2a) offer stable control and simplicity, making them highly maneuverable in confined areas [18]. Hexacopters (Figure 2b) enhance thrust and provide fault tolerance due to their six-rotor redundancy ideal for heavier agricultural payloads . Octocopters (Figure 2c) deliver even higher lift capacity and improved redundancy, though at the cost of added weight and complexity [19]. Tricopters (Figure 2d) emphasize maneuverability with fewer rotors but require more complex control tuning and are less stable [20]. Fixed-wing drones enable long endurance flights and efficient area coverage, yet are limited in confined field operations due to requiring forward motion and larger launch space [21]. A weighted decision matrix, based on safety, payload, stability, maneuverability, impact resistance, manufacturability, and cost, was used to evaluate each configuration. As shown in Table 1, the hexacopter scored the highest (3.25), confirming its suitability due to its superior payload capacity, structural redundancy, and ease of manufacturing. Thus, the hexacopter was selected as the optimal platform for the agricultural drone design. Table 1 Selection of drone frame geometries Selection characteristic Drone frame geometries Wight Quadcopter: Hexacopter Tri copter Octocopter: Fixed-Wing Safety 12 % 2 0.24 3 0.36 1 0.12 3 0.36 4 0.48 Pay load 14% 1 0.14 4 0.56 0 0 3 0.42 4 0.56 Stability and Control 8% 4 0.32 3 0.24 1 0.08 2 0.16 0 0 Manoeuvrability 6% 3 0.18 2 0.12 4 0.24 1 0.06 0 0 Impact 8% 2 0.16 4 0.32 3 0.24 1 0.08 0 0 Manufacturability 9% 4 0.36 3 0.27 2 0.18 1 0.9 0 0 Maintainability 10% 3 0.3 3 0.3 4 0.4 1 0.1 0 0 Complexity 12% 4 0.48 3 0.36 1 0.12 2 0.24 0 0 Cost-effectiveness 11% 3 0.33 3 0.33 4 0.44 1 0.11 0 0 Flight Time 13% 2 0.26 3 0.39 1 0.13 1 0.13 4 0.52 3.2. Manufacturability and Material Selection Table 2 Manufacturability and Material Selection Material PLA ABS PA PP PS PC TPU AS A PET PE EK PE KK PEi Density g/cm^3 1.28 1.08 1.13 0.93 1.05 1.20 1.45 1.08 1.26 1.34 1.28 1.38 Water Absorption % 0.244 0.414 1.710 0.017 0.096 0.186 0.238 0.307 0.16 0.579 0.30 0.22 Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 72 Tensile Strength, Ultimate 62.4 38.7 70.1 26.6 27.4 66.9 64.5 45.5 41.8 99.5 73.1 131 Tensile Strength, 40.5 45 72 26.7 25.9 61.0 63.6 44.4 47.6 98.0 78.1 11 Yield MPa Modulus of Elasticity GPa 2.27 2.05 1.47 1.32 1.99 2.36 2.58 2.13 2.59 4.0 3.32 7.68 Charpy Impact, Unnotched 𝐽/𝑐𝑚2 2.64 15.4 21.0 3.65 7.70 6.0 12.7 7.8 6.94 10.9 4.87 3.20 Deflection Temp. at 0.46 MPa °C 83 94.6 172 96.8 86.8 129 142 96.3 73.5 200 150 280 Max Service 179 89.2 116 122 106 122 141 96.2 67.2 673 257 18 Temp, Air °C The manufacturability of the hexacopter drone frame was a central consideration during the conceptual and detailed design stages. Additive manufacturing was selected due to its design freedom, material efficiency, and ability to produce complex geometries that traditional subtractive methods cannot achieve. Among various 3D printing methods, Fused Deposition Modeling (FDM) was chosen for its accessibility, affordability, and suitability for prototyping drone structures. FDM enables layered deposition of thermoplastic materials, which is ideal for fabricating large, lightweight airframe structures. However, material compatibility with FDM constraints such as thermal shrinkage, layer adhesion, and build platform limitations was rigorously analyzed to ensure printability and mechanical performance [22]. Table 2 Material properties of candidate materials A comprehensive material selection process was followed to identify the optimal thermoplastic for drone frame fabrication. The materials evaluated included PLA, ABS, PA, PP, PS, PC, TPU, ASA, PETG, PEEK, PEKK, and PEI. Their mechanical, thermal, and physical properties were compiled and compared in Table 2, which includes data on density, tensile strength, modulus of elasticity, impact resistance, water absorption, and heat deflection temperature. The data were sourced from publicly available engineering material databases and manufacturer datasheets [23]. 3.3. General specification for design of agricultural drone frame To ensure the drone can achieve stable lift and maneuverability, the propulsion system consisting of motors, propellers, battery, and Electronic Speed Controllers (ESCs) must produce sufficient thrust to overcome the drone’s total weight. Thrust refers to the upward force generated by the propellers and is expressed in Newtons (N), where 1 N≈0.102 kg. To determine the total required thrust, the following values and formulas were used: the total estimated mass of the drone, which includes the frame, a 10-liter liquid payload, pump, and electronics, is defined as Mass drone. To calculate the thrust needed to keep the drone hovering (i.e., to balance its own weight), the following equation was applied [24]: ……………… (1) To ensure the drone can perform take-off and in-flight maneuvers, a thrust-to-weight ratio (TWR) of 1.5 was considered. The total thrust required is given by: ……………….. (2) Substituting the relevant values into Eq. (1) – (3), the calculated results were as follows: The required hovering thrust was 165.59 N (Eq. 1), and the total thrust required for stable operation was 248.4 N (Eq. 2). The drone utilizes a hexacopter frame, meaning it is powered by six motors. To determine the minimum thrust required from each motor, the following formula was applied: …………….. (3) Substituting the relevant values into Eq. (3), the minimum thrust per motor was found to be 41.4 N. These calculated thrust values serve as design criteria for selecting suitable motors and propellers in the subsequent component selection stage. Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 73 In the drone industry, brushless motors are the preferred choice due to their design, which incorporates a permanent magnet rotor rotating around a stationary armature. Compared to brushed DC motors, brushless motors offer superior efficiency, reduced maintenance, higher torque-to-weight ratio, quieter performance, and extended operational life [25]. For the current hexacopter configuration, the T-Motor 4008 KV230 was selected due to its ability to deliver approximately 4.2 kgf (41.4 N) of thrust at full throttle, satisfying the required lift force per motor. This selection was guided by the need to match the drone’s total thrust demand while ensuring compatibility with system parameters [26]. To determine the acceptable performance limits for stable lift, the minimum required thrust per motor was calculated by equally distributing the total thrust across the six motors, using the equation: …………… (4) Substituting the values into Eq. (4), the total thrust requirement of the drone was based on a fully loaded take-off weight of 25.2 kg, multiplied by the gravitational constant (9.81 m/s²), resulting in a total lift force of approximately 247.2 N. Dividing this total thrust by six motors, the minimum required thrust per motor was determined to be 41.4 N. This value confirms that each T-Motor 4008KV230, rated at 41.4 N, provides just enough thrust to ensure stable flight under fullload conditions. This ensures adequate lift capacity while maintaining optimal efficiency for agricultural drone operations [27]. 3.4. Design of geometry The geometry of the drone frame critically affects its structural efficiency, particularly its strength-to-weight balance during flight. In vertical or horizontal motion, the UAV encounters lift, drag, thrust, and weight, with horizontal flight introducing a thrust vector with both vertical and horizontal components. Each arm of the hexacopter acts as a cantilever beam, fixed at the center and bearing the motor and propeller at its tip, thus experiencing downward forces from both gravity and aerodynamic loading. The force along each arm can be modeled as: ………………. (5) where Wtotal is the total drone weight and NNN is the number of arms. The beam’s deflection under load depends on the material’s Young’s Modulus (E), highlighting the importance of material selection in frame design [28]. 3.5. Design of Length Drone Arm The drone arm is modeled as a cantilever beam with an I-beam cross-section, which provides high strength-to-weight efficiency due to its structural geometry. This makes the I-beam a preferred choice for lightweight drone applications. For material selection, ABS (Acrylonitrile Butadiene Styrene) is chosen due to its excellent impact resistance, moderate stiffness, and ease of fabrication. The relevant material properties of ABS used in this study are summarized in Table 3. Table 3 ABS material properties Property Symbol Value Unit Yield Strength σ ≈ 40 MPa Young’s Modulus E ≈ 2,100 MPa(2.1 GPa) Density ρ ≈ 1,040 𝑘𝑔/𝑚3 Poisson’s Ratio ν ≈ 0.37 -- A cantilever beam is fixed at one end while the other remains free, allowing it to support loads and resist bending and deflection. The assumed I-beam geometry includes a flange width b=0.04m, web height h=0.04m, and web thickness t=0.01m. The moment of inertia I for the I-beam cross-section is calculated using Eq. (6): …………. (6) Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 74 Substituting the dimensions, the moment of inertia is found to be: I = 1.933 ∗10−7𝑚4 To ensure structural safety, the deflection δ at the free end of the beam is limited to 5 mm. The deflection of a cantilever beam subjected to a point load P at its free end is given by Eq. (7): …………… (7) By substituting the allowable deflection into Eq. (7), the allowable length of the drone arm is found to be approximately 530 mm. This satisfies the deflection limit, indicating that the arm will remain within safe elastic deformation under operational loading. The maximum bending stress occurs at the fixed end and is computed using Eq. (8): …………. (8) Where M=P⋅L=21.94Nm and c=h/2=0.02m. Substituting these values, the resulting maximum bending stress is: σ=2.26MPa Since this value is significantly below the tensile strength of ABS (approximately 40 MPa), the design is considered safe against bending failure. The shear force along the cantilever beam remains constant due to the point load at the free end, as expressed in V(x)=−P=−41.4N and the bending moment at a distance x from the fixed support is described by M(x)=−P(L−x) this moment linearly increases toward the fixed end, with a maximum magnitude of M(0)=−21.94Nm at the fixed support, indicating tension at the top fibers and compression at the bottom. The transverse shear stress τ is determined using Eq. (9): ……………. (9) Here, Q is the first moment of area about the neutral axis, calculated from contributions of the flange and upper half of the web. The flange area is A1=b⋅t=400mm2, with centroidal distance y1=15mm. The web area above the neutral axis is A2=t⋅(h/2−t)=100mm2, with centroidal distance y2=5mm. The total first moment of area becomes substituting the known values into Eq. (9), the transverse shear stress is:τ=0.1392MPa. This value is significantly lower than the typical shear strength of ABS (20–30 MPa), confirming that the design is safe from shear failure. The I-beam design offers strong resistance to both bending and shear by concentrating material away from the neutral axis (to resist moment) and using a central web (to resist shear). To refine such structural properties, the parallel axis theorem may be employed, particularly for more complex or composite sections. According to Eq. (10), the total moment of inertia can be expressed as: ………………(10) This approach allows for adjusting section stiffness based on geometry and mass distribution, making it a powerful tool in generative design systems. The drone arm is subject to dynamic forces generated by the motor-propeller system, which can induce vibrations. To ensure safe operation, it is critical to evaluate the arm’s natural frequency and compare it with the excitation frequency produced during motor rotation.The motor excitation frequency is calculated as: ………. (11) Using a T-Motor 4008 (KV230) and a 22.2 V battery, the motor speed is: fm=85.1Hz The natural frequency of the arm, modeled as a cantilever beam, is given by: …………….. (12) Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 75 From Eq. (12) we got fn=220.589Hz. Since fn≫fm, the arm is safe from resonance [29]. The impact resistance of a hexacopter drone is crucial for ensuring structural integrity during hard landings, especially in agricultural environments. This analysis evaluates the ability of the landing gear particularly the legs to absorb and dissipate the energy from impact without permanent deformation or failure. The kinetic energy at impact is calculated by considering the conversion of potential energy into kinetic energy during free fall: ……………. (13) Where mmm is the drone mass, g is gravitational acceleration, and h is the landing height. For a drone mass of 16.88 kg and a 10-meter fall, the impact energy is approximately 1655.9 J. To resist this energy, the landing leg’s geometry and material properties play a vital role. The moment of inertia of the leg's rectangular cross-section governs its bending stiffness. While not explicitly needed for this summary, it is used within the stiffness equation and depends heavily on the height and width of the cross-section (height cubed, in particular, greatly influences stiffness). The stiffness of each leg is given by: …………… (14) Where E is Young’s modulus, I is the moment of inertia, and L is the leg length. For ABS plastic and a 0.3 m leg, the stiffness per leg is approximately 24.89 kN/m. With four legs, the total system stiffness is: ……………… (15) The maximum deflection during landing is estimated using energy balance, assuming no energy loss and purely elastic deformation: ………………(16) Substituting the values yields a small deflection of approximately 0.14 mm, indicating high structural rigidity. The impact force transmitted to the structure is: ……………… (17) Resulting in a force of 13.98 kN, which the system must safely absorb. ANSYS simulation (Figure 3) verifies this analytically derived performance. The maximum deformation is 0.00168 m, and the von Mises stress (2.67 MPa) remains well below ABS’s yield strength. Furthermore, the elastic strain of 0.00128 confirms full recovery after landing, without permanent damage. Figure 3 Analysis during landing for a hex copter drone, a) total deformation, b) von mises stress and c) equivalent elastic strain Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 82 Table 6 Validate agricultural hexacopter drone frame to real-world agricultural drones No. Drone Frame Model Frame Mass (kg) Material Hexcopter Frame Mass (kg) Percentage Difference Mass Optimizing Reference 1 DJI Agras T20 3.5 Carbon fiber, composites 2.784 -20.5% (lighter) DJI Agras T20 2 XAG V Series (V40) 4.0 Aluminum alloy, carbon fiber 2.784 -30.4% (lighter) XAG V Series 3 Hanhe 10L Frame 3.0 Carbon fiber 2.784 -7.2% (lighter) Hanhe Frames on AliExpress 4 Fly Dragon FD-10 3.2 Carbon fiber, high-strength plastic 2.784 -13.0% (lighter) Fly Dragon FD-10 on Alibaba 5 Tarot T18 Octocopter 4.5 Aluminum, carbon fiber 2.784 -38.1% (lighter) Tarot T18 on Banggood The comparative table demonstrates that despite using lightweight materials such as aluminum and carbon fiber, commercial frames remain heavier than the proposed design. This indicates the effectiveness of generative design in minimizing excess material while preserving structural integrity. The validated frame outperforms competitors like the Hanhe 10L and Fly Dragon FD-10 by 7.2% and 13.0% respectively. These results are graphically depicted in Figure 15, which confirms that the generative design approach achieves significant reductions in structural mass without compromising functionality or durability. This highlights the frame’s advantage in payload efficiency and confirms its suitability for aerial spraying applications requiring endurance and load minimization. Figure 15 Mass comparison between generative drone frame and commercial agricultural drone frames 5. Conclusion This research successfully demonstrated the potential of generative design in Autodesk Fusion 360 for developing a structurally efficient and lightweight hexacopter drone frame tailored for agricultural spraying applications. By utilizing topology optimization and setting appropriate design constraints, a final frame mass of 2.784 kg was achieved using ABS plastic via FDM 3D printing. This outcome represents a significant mass reduction of 7.2% to 38.1% compared to existing commercial drone frames designed for 10-liter payload capacity. Structural validation through finite element method (FEM) analysis confirmed that the generative frame design maintained acceptable mechanical integrity under realistic loading conditions. The safety factor ranged from 9.60 to 15, and the maximum deformation was limited to Global Journal of Engineering and Technology Advances, 2025, 24(01), 067-085 83 1.288 mm, ensuring mechanical robustness even under motor thrust and payload-induced stresses. Von Mises stress (3.153 MPa) and strain (0.002 mm/mm) levels remained well within the allowable limits for ABS material, with a compressive third principal strain of only –0.002 mm/mm, indicating full elastic behavior and no risk of buckling. Flight testing under variable payloads further demonstrated the energy efficiency of the design. As the drone’s tank gradually emptied from 10 L to 0 L, total mass decreased from 16.88 kg to 6.88 kg, leading to a reduction in throttle from 80% to 32.6%, and extending flight time from 6.0 to 14.6 minutes. This inverse relationship between payload and flight duration was quantitatively captured using graphs of throttle vs. flight time and drone mass vs. flight time. The drone's efficient hover performance, validated by both simulation and real-world trials, confirms that the lightweight generative design enables superior energy usage and prolonged aerial operation—critical factors for agricultural drones performing coverage-intensive spraying. In benchmarking studies, the proposed frame was compared to five real-world commercial drones such as the DJI Agras T20, XAG V40, and Tarot T18. The generative design frame was found to be lighter by 7.2% to 38.1%, depending on the competitor, without sacrificing functionality or structural safety. This positions the design as a strong candidate for lowcost, 3D-printed agricultural drones, especially in emerging economies or remote farming areas where affordability, efficiency, and self-manufacturability are key. The research confirms that integrating generative design and additive manufacturing can revolutionize drone development by enabling lightweight, structurally sound, and customized designs suited for field use. Future work will focus on enhancing drone prototypes with modular payload bays, sensor integration, and AI-based precision spraying. These improvements aim to extend capabilities beyond fluid dispersion by supporting tasks like crop monitoring and adaptive mission planning. Integrating intelligent systems will optimize performance under varying field conditions. This evolution will reinforce the drone’s role in smart agriculture Compliance with ethical standards Acknowledgments The authors extend their sincere appreciation to all individuals and institutions who contributed to the successful completion of this study. Disclosure of conflict of interest The authors declare no potential conflicts of interest concerning the research, authorship, or publication of this article. References [1] M. F. Aslan, A. Durdu, K. Sabanci, E. Ropelewska, and S. S. Gültekin, "A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses," Applied Sciences, vol. 12, no. 3, p. 1047, 2022, doi: https://doi.org/10.3390/app12031047. [2] K. Nonami, F. Kendoul, S. Suzuki, W. Wang, and D. 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