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Based on wavelet-Lipschitz function for node detection method on armor subsequent damage optimization

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Abstract

In this paper, a node detection method is proposed to detect the damaged parts of armor automatically. The traditional detection of armor damage is judged by experience first, and then the parts are decomposed and confirmed, which makes the problem solving time long and efficiency low. In this paper, the in-mold electronic decoration technology is used on the surface of armor, and the node displacement changes after the surface film is damaged are used to achieve the purpose of damage detection. By using the wavelet analysis, the singularity of the wavelet function can be found and the damaged part can be determined. The Lipschitz index can be used to judge the singularity of the wavelet function to detect the local damage on the armor surface. Finally, the changes of Lipschitz index of the signal with different damage degrees on the helmet deck were simulated. In terms of influencing factors of optimization process, moldex3D was used to simulate and analyze the damaged parts of armor—different injection molding parameter schemes were set to optimize the armor film. In the first stage, the displacement change at the damaged location is detected by the probe node through simulation. In the second stage, the armor was simulated by setting appropriate process parameters such as melt temperature, filling time, filling pressure, and filling time. In the third stage, the wavelet analysis and a Lipschitz index were used to detect the location of damaged nodes. In the fourth stage, the change curve of wavelet analysis is verified by the analysis experiment. Through the experimental verification, it can be seen that the position displacement of the damaged armor changes and the singularity is generated on the wavelet function to achieve the purpose of damage detection. The influencing factors of the film injection molding are temperature and pressure. Through the simulation analysis, we optimize the injection molding parameters of the film, thus improving the damage resistance of the armor. Finally, the optimal parameters of vulnerable parts are obtained and the optimization scheme is determined.

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References

  1. Cha Y-J, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput-Aided Civil Infrastruct Eng 32:361–378. https://doi.org/10.1111/mice.12263

    Article  Google Scholar 

  2. Gomes GF, Mendéz YAD, Alexandrino PDSL, da Cunha SS, Ancelotti AC (2018) The use of intelligent computational tools for damage detection and identification with an emphasis on composites – a review. Compos Struct 196:44–54. https://doi.org/10.1016/j.compstruct.2018.05.002

    Article  Google Scholar 

  3. Lin J-F, Xu Y-L, Law S-S (2018) Structural damage detection-oriented multi-type sensor placement with multi-objective optimization. J Sound Vib 422:568–589. https://doi.org/10.1016/j.jsv.2018.01.047

    Article  Google Scholar 

  4. Tarfaoui M, El Moumen A, Ben Yahia H (2018) Damage detection versus heat dissipation in E-glass/Epoxy laminated composites under dynamic compression at high strain rate. Compos Struct 186:50–61. https://doi.org/10.1016/j.compstruct.2017.11.083

    Article  Google Scholar 

  5. Wu CH, Li JH (2020) The use of 3D in-mold decoration technology to form a film with printed circuits. Polym Eng Sci 60:2517–2528. https://doi.org/10.1002/pen.25490

    Article  Google Scholar 

  6. Genest M, Krys D, Mandache C (2020) Capacitive sensing for the detection of tile misalignment in ceramic armor arrays. NDT & E Int 112:102261

    Article  Google Scholar 

  7. Gao J, Kedir N, Kirk CD, Hernandez J, Wang J, Paulson S, Zhai X, Horn T, Kim G, Gao J et al (2021) Real-time damage characterization for GFRCs using high-speed synchrotron X-ray phase contrast imaging. Compos Part B: Eng 207:108565. https://doi.org/10.1016/j.compositesb.2020.108565

    Article  Google Scholar 

  8. Mishra RB, El-Atab N, Hussain AM, Hussain MM (2021) Recent progress on flexible capacitive pressure sensors: from design and materials to applications. Adv Mater Technol 6:2001023. https://doi.org/10.1002/admt.202001023

    Article  Google Scholar 

  9. Bjørheim F, Siriwardane SC, Pavlou D (2022) A review of fatigue damage detection and measurement techniques. Int J Fatigue 154:106. https://doi.org/10.1016/j.ijfatigue.2021.106556

    Article  Google Scholar 

  10. Chang H, Zhang G, Sun Y, Lu S (2022) A Node detection method based on Johnson-Cook and thin-film IMD characteristic model armor damage detection repair and subsequent optimization. Polymers 14:4540. https://doi.org/10.3390/polym14214540

    Article  Google Scholar 

  11. Montanari L, Basu B, Spagnoli A, Broderick BM (2015) A padding method to reduce edge effects for enhanced damage identification using wavelet analysis. Mech Syst Signal Process 52–53:264–277. https://doi.org/10.1016/j.ymssp.2014.06.014

    Article  Google Scholar 

  12. GhanbariMardasi A, Wu N, Wu C (2018) Experimental study on the crack detection with optimized spatial wavelet analysis and windowing. Mech Syst Signal Process 104:619–630. https://doi.org/10.1016/j.ymssp.2017.11.039

    Article  Google Scholar 

  13. Katunin A, Lopes H, dos Santos JVA (2019) Identification of multiple damage using modal rotation obtained with shearography and undecimated wavelet transform. Mech Syst Signal Process 116:725–740. https://doi.org/10.1016/j.ymssp.2018.07.024

    Article  Google Scholar 

  14. Knitter-Piątkowska A, Dobrzycki A (2020) Application of wavelet transform to damage identification in the steel structure elements. Appl Sci 10:8198. https://doi.org/10.3390/app10228198

    Article  Google Scholar 

  15. Gad SI, Attia MA, Hassan MA, El-Shafei AG (2021) A random microstructure-based model to study the effect of the shape of reinforcement particles on the damage of elastoplastic particulate metal matrix composites. Ceram Int 47:3444–3461. https://doi.org/10.1016/j.ceramint.2020.09.189

    Article  Google Scholar 

  16. Zhou K, Lei D, He J, Zhang P, Bai P, Zhu F (2021) Real-time localization of micro-damage in concrete beams using DIC technology and wavelet packet analysis. Cem Concr Compos 123:104198. https://doi.org/10.1016/j.cemconcomp.2021.104198

    Article  Google Scholar 

  17. Jahangir H, Hasani H, Esfahani MR (2021) Wavelet-based damage localization and severity estimation of experimental RC beams subjected to gradual static bending tests. Structures 34:3055–3069. https://doi.org/10.1016/j.istruc.2021.09.059

    Article  Google Scholar 

  18. Chang H, Zhang G, Sun Y, Lu S (2022) Using sequence-approximation optimization and radial-basis-function network for brake-pedal multi-target warping and cooling. Polymers (Basel) 14:2578. https://doi.org/10.3390/polym14132578

    Article  Google Scholar 

  19. Dai B, Wang P, Wang T-Y, You C-W, Yang Z-G, Wang K-J, Liu J-S (2017) Improved terahertz nondestructive detection of debonds locating in layered structures based on wavelet transform. Compos Struct 168:562–568. https://doi.org/10.1016/j.compstruct.2016.10.118

    Article  Google Scholar 

  20. Zhu L-F, Ke L-L, Zhu X-Q, Xiang Y, Wang Y-S (2019) Crack identification of functionally graded beams using continuous wavelet transform. Compos Struct 210:473–485. https://doi.org/10.1016/j.compstruct.2018.11.042

    Article  Google Scholar 

  21. Sha G, Radzienski M, Soman R, Cao M, Ostachowicz W, Xu W (2020) Multiple damage detection in laminated composite beams by data fusion of Teager energy operator-wavelet transform mode shapes. Compos Struct 235:111798

    Article  Google Scholar 

  22. Allahverdi F, Ramezani A, Forouzanfar M (2019) Sensor fault detection and isolation for a class of uncertain nonlinear system using sliding mode observers. Automatika 61:219–228. https://doi.org/10.1080/00051144.2019.1706911

    Article  Google Scholar 

  23. Ren J, Feng L, Fu J, Zhuang T (2021) Admissibility analysis and passive output feedback control for one-sided Lipschitz nonlinear singular Markovian jump systems with uncertainties. Appl Math Comput 409:126405. https://doi.org/10.1016/j.amc.2021.126405

    Article  MathSciNet  MATH  Google Scholar 

  24. Jalil B, Jalil Z, Fauvet E, Laligant O (2021) Edge-preserving image denoising based on Lipschitz estimation. Appl Sci 11:5126. https://doi.org/10.3390/app11115126

    Article  Google Scholar 

  25. Chang H, Zhang G, Sun Y, Lu S (2022) Non-dominant genetic algorithm for multi-objective optimization design of unmanned aerial vehicle shell process. Polymers (Basel) 14:2896. https://doi.org/10.3390/polym14142896

    Article  Google Scholar 

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Acknowledgements

This research was supported by CoreTech System Co., Ltd. (Moldex3D) and Yizumi Precision Machinery Co., Ltd., which are gratefully acknowledged.

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Authors and Affiliations

  1. Department of Mechanical Engineering, College of Engineering, Shantou University, Shantou, 515063, China

    Hanjui Chang, Yue Sun, Shuzhou Lu & Guangyi Zhang

  2. Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou, 515063, China

    Hanjui Chang, Yue Sun, Shuzhou Lu & Guangyi Zhang

Authors
  1. Hanjui Chang
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  2. Yue Sun
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  3. Shuzhou Lu
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  4. Guangyi Zhang
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Contributions

Hanjui Chang did conceptualization. Guangyi Zhang curated the data. Shuzhou Lu is responsible for methodology. Yue Sun is assigned to project administration. Hanjui Chang did writing—original draft. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

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Correspondence to Hanjui Chang.

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Chang, H., Sun, Y., Lu, S. et al. Based on wavelet-Lipschitz function for node detection method on armor subsequent damage optimization. Int J Adv Manuf Technol 127, 4163–4180 (2023). https://doi.org/10.1007/s00170-023-11734-1

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  • DOI: https://doi.org/10.1007/s00170-023-11734-1

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