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Depth Accurate Prediction and Kerf Quality Improvement of CFRP Through-Hole Laser Cutting via Acoustic Emission Nondestructive Monitoring Technology

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Abstract

Acoustic emission (AE) technology is an effective method for monitoring the quality of carbon fiber reinforced plastic (CFRP) laser cutting. It is a challenge to decipher the formation mechanism because of the heterogeneity and anisotropy of CFRP. By analyzing the characteristics of AE signals and the images captured by the high-speed camera under different parameters, four types of AE signal sources were found: high-strength carbon fiber fractures, uniform resin ablation, photochemical fractures, and plasma plume impact. Kerf depth can be accurately identified by measuring the amplitude of the AE signal, and the experimental results verify that the error between the predicted and the experiment values is less than 0.1 mm. The laser defocusing amount, the width of the Heat-affected zone (HAZ), and the overall cutting time can be accurately determined by measuring the Root Mean Square (RMS) of the AE signal. The focus position can be adjusted before the laser cutting, decreasing the width of HAZ from 211.3 μm to 131.5 μm and the time of through-hole cutting from 245 s to 207 s.

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All data generated or analysed during this study are included in this published article (and its supplementary information files).

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Acknowledgements

This work is supported by National Nature Science Foundation of China (Grant No. 52188102, 52305468, and 52375433), the China Postdoctoral Science Foundation (Grant No. 2023M731193), “Unveiling and Leading” Project of Wuhan Donghu New Technology Development Zone (2022KJB107), Major science and technology project of Hubei Province (2022AAA008), Guangdong Basic and Applied Basic Research Foundation (2020A1515011393), Open Project Program of Wuhan National Laboratory for Optoelectronics (2021WNL0KF018), Major program (JD) of Hubei Province (2023BAA0015), Guangdong Basic and Applied Basic Research Foundation (2023A1515012639).

Funding

This work is supported by National Nature Science Foundation of China (Grant No. 52188102, 52305468, and 52375433), the China Postdoctoral Science Foundation (Grant No. 2023M731193), “Unveiling and Leading” Project of Wuhan Donghu New Technology Development Zone (2022KJB107), Major science and technology project of Hubei Province (2022AAA008), Guangdong Basic and Applied Basic Research Foundation (2020A1515011393), Open Project Program of Wuhan National Laboratory for Optoelectronics (2021WNL0KF018), Major program (JD) of Hubei Province (2023BAA0015), Guangdong Basic and Applied Basic Research Foundation (2023A1515012639).

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Authors and Affiliations

  1. National Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China

    Long Chen, Youmin Rong, Jiajun Xu, Yu Huang, Wenyuan Li, Chunmeng Chen & Zhihui Yang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Long Chen, Youmin Rong, Jiajun Xu, Yu Huang, Wenyuan Li, Chunmeng Chen & Zhihui Yang

  3. Aeronautical Engineering College, Air Force Engineering University, **’an, China

    Song Shu

  4. Wuhan HGlaser Engineering Co.,Ltd, Wuhan, China

    Siyang Cao

Authors
  1. Long Chen
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Contributions

Long Chen: Writing- Original draft preparation, Investigation, Conceptualization, Writing- Reviewing and Editing, Data curation, Validation.Youmin Rong: Data curation, Visualization, Writing- Reviewing and Editing, Supervision, SoftwareConceptualization, Methodology. Song Shu: Conceptualization, Methodology, Supervision, Funding acquisition.Jiajun Xu: Methodology, Resources, Software, Funding acquisition, Supervision.Yu Huang: Software, Resources, Funding acquisition, Supervision, Validation. Wenyuan Li: Writing- Reviewing and Editing, Supervision, Data curation.Chunmeng Chen: Software, Data curation, Investigation.Zhihui Yang: Data curation, Software, Validation.Siyang Cao: Validation, Resources.

Corresponding author

Correspondence to Jiajun Xu.

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Chen, L., Rong, Y., Shu, S. et al. Depth Accurate Prediction and Kerf Quality Improvement of CFRP Through-Hole Laser Cutting via Acoustic Emission Nondestructive Monitoring Technology. J Nondestruct Eval 43, 52 (2024). https://doi.org/10.1007/s10921-024-01082-9

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