Academic Journal

Generative Design, Simulation, and 3D Printing of the Quadcopter Drone Frame.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Generative Design, Simulation, and 3D Printing of the Quadcopter Drone Frame.
Συγγραφείς: Andries, Victor, Zaharia, Sebastian-Marian
Πηγή: Applied Sciences (2076-3417); Sep2025, Vol. 15 Issue 17, p9647, 22p
Θεματικοί όροι: Aerospace engineering, Drone aircraft, Rapid prototyping, Structural optimization, Three-dimensional printing, Structural reliability, Performance-based design, New product development
Περίληψη: The frame of a quadcopter drone is the most important component, as it supports all other systems and plays a vital structural role, supporting the stresses that appear during flight. The objective was to reduce the weight of the frame (18% reduction compared to the original version manufactured from carbon fibre), while maintaining structural integrity, by using an integrated strategy that includes optimizing the frame shape according to the components used, the stresses it must withstand, and considerations related to design for additive manufacturing. The optimization of the quadcopter drone frame was achieved using generative design and additive technologies (3D printing) and represents a cutting-edge approach in aerospace engineering, which allows for overcoming the limitations of traditional manufacturing methods. This study successfully completed all stages of the aeronautical product development cycle, from preliminary design, generative design, structural analysis, and 3D printing to assembly and functionality testing. The integration of generative design and 3D printing into the aeronautical product development cycle represents a complex and feasible challenge, with advantages in terms of efficiency, performance, and innovation capacity that fully justify the effort. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Generative Design, Simulation, and 3D Printing of the Quadcopter Drone Frame.
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  Data: The frame of a quadcopter drone is the most important component, as it supports all other systems and plays a vital structural role, supporting the stresses that appear during flight. The objective was to reduce the weight of the frame (18% reduction compared to the original version manufactured from carbon fibre), while maintaining structural integrity, by using an integrated strategy that includes optimizing the frame shape according to the components used, the stresses it must withstand, and considerations related to design for additive manufacturing. The optimization of the quadcopter drone frame was achieved using generative design and additive technologies (3D printing) and represents a cutting-edge approach in aerospace engineering, which allows for overcoming the limitations of traditional manufacturing methods. This study successfully completed all stages of the aeronautical product development cycle, from preliminary design, generative design, structural analysis, and 3D printing to assembly and functionality testing. The integration of generative design and 3D printing into the aeronautical product development cycle represents a complex and feasible challenge, with advantages in terms of efficiency, performance, and innovation capacity that fully justify the effort. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Sep2025
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