Skip to content
2000
image of Optimization of Blade Injection Molding for Plant Protection UAV Based on Blade Element Theory and Response Surface Method

Abstract

Background

The method was proposed to optimize the warpage and volume shrinkage of the blade of a plant protection unmanned aerial vehicle (UAV) during injection molding. The influence of molding defects on the geometric parameters of airfoil was analyzed from two aspects of blade geometry structure and molding process based on blade element theory. This paper focuses on reducing the warpage and volume shrinkage of the blade in the production process and improving molding quality through moldflow analysis and calculation.

Objective

The purpose of this patent study was to combine injection molding CAE with orthogonal test and response surface method to reduce product warpage and volume shrinkage, improve dimensional accuracy and production efficiency, and combine fluid dynamics simulation experiments to prove the experiment's reliability.

Methodology

The optimization method of injection molding process parameters was proposed based on the orthogonal test and response surface method, combining the principles of aerodynamics mathematics and polymer injection molding. The Taguchi experiment was designed with melt temperature injection time and mold temperature pressure holding time as the optimization variable. The Box-Behnken Design (BBD) experiment was designed, and the response surface model was established with Design-Expert software to analyze the mapping relationship between process parameters and warpage and volume shrinkage. Moldflow software was used for flow analysis on the basis of analysis by response surface method, and the deformed parts were analyzed by computational fluid dynamics (CFD) with ANSYS software to prove the reliability of the calculation process.

Results

The warpage and volume shrinkage of the product can be significantly reduced based on the orthogonal test and response surface model. The aerodynamic performance of the blade can be improved by optimization injection molding, making the actual production of the blade close to the original design blade model to the greatest extent and reducing the actual production trial and error cost.

Conclusion

An optimization method of the injection molding process of the UAV rotor was proposed based on the response surface method and blade element theory. Through the systematic design and optimization of injection molding parameters, the molding quality and performance of the rotor can be improved, which provides a new way to optimize the dynamic components of mass-produced UAVs.

Loading

Article metrics loading...

/content/journals/eng/10.2174/0118722121322874240708112349
2024-12-04
2025-01-15
Loading full text...

Full text loading...

References

  1. Chang L.S. Zhang Q.Y. Guo X.Y. Airfoil optimization design based on Gaussian process regression and genetic algorithm. Chinese J. Aerospace Power 2020 36 2306 2316 10.13224/j.cnki.jasp.20200402
    [Google Scholar]
  2. Cruzatty C. Sarmiento E. Valencia E. Cando E. Design methodology of a UAV propeller implemented in monitoring activities. Mater. Today Proc. 2022 49 115 121 10.1016/j.matpr.2021.07.481
    [Google Scholar]
  3. Zang Y. He X.G. Zhou Z.Y. Comprehensive evaluation method for lifting characteristics of electric multi-rotor UAV for plant protection. Nongye Gongcheng Xuebao 2018 34 69 77 10.11975/j.issn.1002‑6819.2018.14.009
    [Google Scholar]
  4. Yang X. Ma D. Zhang L. Yu Y. Yao Y. Yang M. High-fidelity multi-level efficiency optimization of propeller for high altitude long endurance UAV. Aerosp. Sci. Technol. 2023 133 108142 10.1016/j.ast.2023.108142
    [Google Scholar]
  5. Kwon H.I. Yi S. Choi S. Kim K. Design of efficient propellers using variable-fidelity aerodynamic analysis and multilevel optimization. J. Propuls. Power 2015 31 4 1057 1072 10.2514/1.B35097
    [Google Scholar]
  6. Lee H.M. Ryu J.K. Ahn S.J. Aerodynamic design optimization of uav rotor blades using a genetic algorithm and artificial neural networks J. Comput. Fluids Eng. 2014 19 3 29 36 10.6112/kscfe.2014.19.3.029
    [Google Scholar]
  7. Xiang S. Zhao L. Chen H.L. A design method for unmanned aerial vehicle rotor. Chinese Aeronaut.Sci. Technol. 2022 33 1 6
    [Google Scholar]
  8. Tadros M. Ventura M. Guedes Soares C. Design of propeller series optimizing fuel consumption and propeller efficiency. J. Mar. Sci. Eng. 2021 9 11 1226 10.3390/jmse9111226
    [Google Scholar]
  9. Wu X. Zuo Z. Ma L. Zhang W. Multi-fidelity neural network-based aerodynamic optimization framework for propeller design in electric aircraft. Aerosp. Sci. Technol. 2024 146 108963 10.1016/j.ast.2024.108963
    [Google Scholar]
  10. Larrabee E.E. Practical design of minimum induced loss propellers. SAE Trans. 1979 ••• 2053 2062 10.4271/790585
    [Google Scholar]
  11. Adkins C.N. Liebeck R.H. Design of optimum propellers. J. Propuls. Power 1994 10 5 676 682 10.2514/3.23779
    [Google Scholar]
  12. Song M.C. Liu Z. Wang M.J. Yu T.M. Zhao D.Y. Research on effects of injection process parameters on the molding process for ultra-thin wall plastic parts. J. Mater. Process. Technol. 2007 187-188 668 671 10.1016/j.jmatprotec.2006.11.103
    [Google Scholar]
  13. Mohan M. Ansari M.N.M. Shanks R.A. Review on the effects of process parameters on strength, shrinkage, and warpage of injection molding plastic component. Polym. Plast. Technol. Eng. 2017 56 1 1 12 10.1080/03602559.2015.1132466
    [Google Scholar]
  14. Wang Q. Zhang J.P. Wang X.W. Deformation pre-compensation of injection molded thin-walled parts based on complex method. Jisuanji Jicheng Zhizao Xitong 2023 29 3692 3703
    [Google Scholar]
  15. Kashyap S. Datta D. Process parameter optimization of plastic injection molding: a review. International Journal of Plastics Technology 2015 19 1 1 18 10.1007/s12588‑015‑9115‑2
    [Google Scholar]
  16. Wu C.H. Liang W.J. Effects of geometry and injection-molding parameters on weld-line strength. Polym. Eng. Sci. 2005 45 7 1021 1030 10.1002/pen.20369
    [Google Scholar]
  17. Liu L. Cao C. LV, Q.Y. “Analysis and Optimization of Forming Precision of Axial Fan Blade Based on DOE and RSM”. Materials Reports 2022 36 262 266 10.11896/cldb.20090121
    [Google Scholar]
  18. Jaiganesh V. Manivannan S. Manivannan S. Numerical analysis and simulation of nylon composite propeller for aircraft. Procedia Eng. 2014 97 1079 1088 10.1016/j.proeng.2014.12.386
    [Google Scholar]
  19. Rutkay B. Laliberté J. Design and manufacture of propellers for small unmanned aerial vehicles. J. Unmanned Veh. Syst. 2016 4 4 228 245 10.1139/juvs‑2014‑0019
    [Google Scholar]
  20. Zhang W.G. Sun J.F. Zhao Q.J. Aerodynamic design and verification methods of rotor airfoils Acta Aerodyn. Sini. 2021 39 6 136 148 10.7638/kqdlxxb‑2021.0315
    [Google Scholar]
  21. Kovačević A. Svorcan J. Hasan M.S. Ivanov T. Jovanović M. Optimal propeller blade design, computation, manufacturing and experimental testing. Aircr. Eng. Aerosp. Technol. 2021 93 8 1323 1332 10.1108/AEAT‑03‑2021‑0091
    [Google Scholar]
  22. Rostami M. Farajollahi A. Aerodynamic performance of mutual interaction tandem propellers with ducted UAV. Aerosp. Sci. Technol. 2021 108 106399 10.1016/j.ast.2020.106399
    [Google Scholar]
  23. Zhu H. Jiang Z. Zhao H. Pei S. Li H. Lan Y. Aerodynamic performance of propellers for multirotor unmanned aerial vehicles: Measurement, analysis, and experiment. Shock Vib. 2021 2021 1 11 10.1155/2021/9538647
    [Google Scholar]
  24. Ning A. Using blade element momentum methods with gradient-based design optimization. Struct. Multidiscipl. Optim. 2021 64 2 991 1014 10.1007/s00158‑021‑02883‑6
    [Google Scholar]
  25. Benini E. Toffolo A. Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation. J. Sol. Energy Eng. 2002 124 4 357 363 10.1115/1.1510868
    [Google Scholar]
  26. Meng K. Wu Q. Xu J. Chen W. Feng Z. Schober R. Swindlehurst A.L. UAV-enabled integrated sensing and communication: opportunities and challenges. IEEE Wirel. Commun. 2024 31 2 97 104 10.1109/MWC.131.2200442
    [Google Scholar]
  27. Meng K. Wu Q. Ma S. Chen W. Quek T.Q.S. UAV trajectory and beamforming optimization for integrated periodic sensing and communication. IEEE Wirel. Commun. Lett. 2022 11 6 1211 1215 10.1109/LWC.2022.3161338
    [Google Scholar]
  28. Chowdary A. Bazzi A. Chafii M. On hybrid radar fusion for integrated sensing and communication. IEEE Trans. Wirel. Commun. 2024 January 1 1 10.1109/TWC.2024.3357573
    [Google Scholar]
  29. Bazzi A. Chafii M. On outage-based beamforming design for dual-functional radar-communication 6g systems. IEEE Trans. Wirel. Commun. 2023 22 8 5598 5612 10.1109/TWC.2023.3235617
    [Google Scholar]
/content/journals/eng/10.2174/0118722121322874240708112349
Loading
/content/journals/eng/10.2174/0118722121322874240708112349
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test