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Effect of Grinding Parameters on Industrial Robot Grinding of CFRP and Defect Formation Mechanism

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Abstract

The use of industrial robots for grinding CFRP is a green processing method. This method not only allows in-situ repair to reduce unnecessary waste of resources, but also produces no excessive contaminants. The effect of various process parameters, including grinding directions, the mesh size of grinding heads and rotating speed, on the grinding quality of Carbon Fiber Reinforced Polymers (CFRP) using industrial robots was investigated. The mechanism of grinding defects was also studied. According to the experimental results, the CFRP grinding process is mainly controlled by the rotating speed, number of grinding heads, and grinding direction. In particular, high-speed grinding helps to improve the surface quality of CFRP. In turn, the use of diamond grinding heads with too small or too large particles may reduce surface quality. Grinding quality changes with the grinding direction. In the grinding direction between 0° and 90°, the surface roughness increases with the angle (but drops at 60°), and The same trend is observed in the grinding direction between 90° and 150°, whereby the surface roughness increases with the angle (but drops at 120°). The surface quality of CFRP is thereby improved after grinding in the direction of 0°, 60°, 120° and 180°. Furthermore, the fiber pull-out occurs, when the feed direction and fiber orientation are aligned. Finally, the low-frequency vibration easily causes fiber pull-out defects.

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The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

  1. Wang, M., Song, Y. M., Wang, P. F., Chen, Y. C., & Sun, T. (2022). Grinding/cutting technology and equipment of multi-scale casting parts. Chinese Journal of Mechanical Engineering, 35(1), 9.

    Article  ADS  Google Scholar 

  2. de Melo, E. G., Silva, J. C. D., Klein, T. B., Polte, J., Uhlmann, E., & Gomes, J. D. (2021). Evaluation of carbon fiber reinforced polymer—CFRP—machining by applying industrial robots. Science and Engineering of Composite Materials, 28(1), 285–298.

    Article  Google Scholar 

  3. Kim, D. G., & Yang, S. H. (2023). Efficient analysis of CFRP cutting force and chip formation based on cutting force models under various cutting conditions. International Journal of Precision Engineering and Manufacturing, 24(7), 1235–1251.

    Article  Google Scholar 

  4. Zou, F., Zhong, B. F., Zhang, H., An, Q. L., & Chen, M. (2022). Machinability and surface quality during milling CFRP laminates under dry and supercritical CO2-based cryogenic conditions. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(3), 765–781.

    Article  Google Scholar 

  5. Liu, C. L., Ren, J. X., Zhang, Y. L., & Shi, K. N. (2023). the effect of tool structure and milling parameters on the milling quality of CFRP based on 3D surface roughness. International Journal of Precision Engineering and Manufacturing, 24(6), 931–944.

    Article  Google Scholar 

  6. Mullin, R., Farhadmanesh, M., Ahmadian, A., & Ahmadi, K. (2020). Modeling and identification of cutting forces in milling of carbon fibre reinforced Polymers. Journal of Materials Processing Technology, 280, 116595.

    Article  CAS  Google Scholar 

  7. Soo, S. L., Shyha, I. S., Barnett, T., Aspinwall, D. K., & Sim, W.-M. (2012). Grinding performance and workpiece integrity when superabrasive edge routing carbon fibre reinforced plastic (CFRP) composites. CIRP Annals, 61(1), 295–298.

    Article  Google Scholar 

  8. Gao, T., Li, C., Jia, D., Zhang, Y., Yang, M., Wang, X., & Xu, X. (2020). Surface morphology assessment of CFRP transverse grinding using CNT nanofluid minimum quantity lubrication. Journal of Cleaner Production, 277, 123328.

    Article  CAS  Google Scholar 

  9. Gao, H., Ma, H. L., Bao, Y. J., Yuan, H. P., & Kang, R. K. (2010). Theoretical analysis of grinding temperature field for carbon fiber reinforced plastics. Advanced Materials Research, 126–128, 52–57.

    Article  Google Scholar 

  10. Altin Karataş, M., & Gökkaya, H. (2018). A review on machinability of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials. Defence Technology, 14(4), 318–326.

    Article  Google Scholar 

  11. Balaji, R., Sivakumar, S., Nadarajan, M., & Selokar, A. (2019). A recent investigations: Effect of surface grinding on CFRP using rotary ultrasonic machining. Materials Today: Proceedings, 18, 5209–5218.

    CAS  Google Scholar 

  12. Wang, H., Pei, Z. J., & Cong, W. L. (2020). A mechanistic cutting force model based on ductile and brittle fracture material removal modes for edge surface grinding of CFRP composites using rotary ultrasonic machining. International Journal of Mechanical Sciences, 176, 13.

    Article  Google Scholar 

  13. Wang, H., Pei, Z. J., & Cong, W. (2020). A feeding-directional cutting force model for end surface grinding of CFRP composites using rotary ultrasonic machining with elliptical ultrasonic vibration. International Journal of Machine Tools and Manufacture, 152, 103540.

    Article  Google Scholar 

  14. Zheng, F., Kang, R., Dong, Z., Guo, J., Liu, J., & Zhang, J. (2018). A theoretical and experimental investigation on ultrasonic assisted grinding from the single-grain aspect. International Journal of Mechanical Sciences, 148, 667–675.

    Article  Google Scholar 

  15. Ning, F., Wang, H., & Cong, W. (2019). Rotary ultrasonic machining of carbon fiber reinforced plastic composites: A study on fiber material removal mechanism through single-grain scratching. The International Journal of Advanced Manufacturing Technology, 103(1–4), 1095–1104.

    Article  Google Scholar 

  16. Voss, R., Seeholzer, L., Kuster, F., & Wegener, K. (2019). Analytical force model for orthogonal machining of unidirectional carbon fibre reinforced polymers (CFRP) as a function of the fibre orientation. Journal of Materials Processing Technology, 263, 440–469.

    Article  CAS  Google Scholar 

  17. Gao, T., Li, C., Yang, M., Zhang, Y., Jia, D., Ding, W., & Wang, J. (2021). Mechanics analysis and predictive force models for the single-diamond grain grinding of carbon fiber reinforced polymers using CNT nano-lubricant. Journal of Materials Processing Technology, 290, 116976.

    Article  CAS  Google Scholar 

  18. Hu, N. S., & Zhang, L. C. (2004). Some observations in grinding unidirectional carbon fibre-reinforced plastics. Journal of Materials Processing Technology, 152(3), 333–338.

    Article  CAS  Google Scholar 

  19. Handa, D., & Sooraj, V. S. (2019). An eccentric sleeve grinding strategy for fibre-reinforced composites. Composites Part B: Engineering., 176, 107332.

    Article  Google Scholar 

  20. Kodama, H., Okazaki, S., Jiang, Y. F., Yoden, H., & Ohashi, K. (2020). Thermal influence on surface layer of carbon fiber reinforced plastic (CFRP) in grinding. Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology, 65, 53–63.

    Google Scholar 

  21. Ning, H., Zheng, H., Zhang, S., & Yuan, X. (2021). Milling force prediction model development for CFRP multidirectional laminates and segmented specific cutting energy analysis. The International Journal of Advanced Manufacturing Technology, 113(9–10), 2437–2445.

    Article  Google Scholar 

  22. Ito, Y., Kita, Y., Fukuhara, Y., Nomura, M., & Sasahara, H. (2022). Development of in-process temperature measurement of grinding surface with an infrared thermometer. Journal of Manufacturing and Materials Processing, 6(2), 23.

    Article  Google Scholar 

  23. Yang, L., Fu, Y. C., Xu, J. H., & Liu, Y. T. (2015). Structural design of a carbon fiber-reinforced polymer wheel for ultra-high speed grinding. Materials & Design, 88, 827–836.

    Article  CAS  Google Scholar 

  24. Yang, L., Chu, C. H., Fu, Y. C., Xu, J. H., & Liu, Y. T. (2015). CFRP Grinding wheels for high speed and ultra-high speed grinding: a review of current technologies and research strategies. International Journal of Precision Engineering and Manufacturing, 16(12), 2599–2606.

    Article  Google Scholar 

  25. Kim, S. M., Ha, J. H., Jeong, S. H., & Lee, S. K. (2001). Effect of joint conditions on the dynamic behavior of a grinding wheel spindle. Int Journal Machine Tools Manufacture, 41(12), 1749–1761.

    Article  Google Scholar 

  26. Qi, J. D., Zhang, D. H., Li, S., & Chen, B. (2017). Modeling of the working accuracy for robotic belt grinding system for turbine blades. Advanced Mechanical Engineering, 9(6), 12.

    Article  Google Scholar 

  27. **e, H., Li, W. L., Zhu, D. H., Yin, Z. P., & Ding, H. (2020). A Systematic model of machining error reduction in robotic grinding. Ieee-Asme Transcations Mechatronics, 25(6), 2961–2972.

    Article  Google Scholar 

  28. Li, W. L., **e, H., Zhang, G., Yan, S. J., & Yin, Z. P. (2016). 3-D Shape matching of a blade surface in robotic grinding applications. Ieee-ASME Transacations Mechatronics, 21(5), 2294–2306.

    Article  Google Scholar 

  29. Dias, E. A., Pereira, F. B., Ribeiro Filho, S. L. M., & Brandão, L. C. (2016). Monitoring of through-feed centreless grinding processes with acoustic emission signals. Measurement, 94, 71–79.

    Article  ADS  Google Scholar 

  30. Svinth, C. N., Wallace, S., Stephenson, D. B., Kim, D., Shin, K., Kim, H. Y., & Kim, T. G. (2022). Identifying abnormal CFRP holes using both unsupervised and supervised learning techniques on in-process force current, and vibration signals. International Journal Precision Engneering Manufacturing, 23(10), 1225–1225.

    Article  Google Scholar 

  31. Lee, C.-H., Jwo, J.-S., Hsieh, H.-Y., & Lin, C.-S. (2020). An Intelligent system for grinding wheel condition monitoring based on machining sound and deep learning. IEEE Access, 8, 58279–58289.

    Article  Google Scholar 

  32. Yan, C. R., Chen, Y., Qian, N., Guo, N., Wang, Y. Q., Yang, H. J., & Zhao, B. (2022). Adaptive approaches to identify the interface in low frequency vibration-assisted drilling of CFRP/Ti6Al4V stacks. International Journal Precision Engneering Manufacturing, 23(8), 895–909.

    Article  Google Scholar 

  33. Guo, W., Wu, C., Ding, Z., & Zhou, Q. (2021). Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding. International Journal Advanced Manufacturing Technology, 112(9–10), 2853–2871.

    Article  Google Scholar 

  34. Huang, W. Z., Li, Y., Wu, X., & Shen, J. N. (2023). The wear detection of mill-grinding tool based on acoustic emission sensor. International Journal Advanced Manufacturing Technology, 124(11–12), 4121–4130.

    Article  Google Scholar 

  35. Kong, X., **u, S., Sun, C., Yao, Y., Zou, X., & Zhao, Y. (2022). Study on the relevance of strengthened layer and vibration signal in grinding-strengthening process. International Journal Advanced Manufacturing Technology, 121(11–12), 7963–7982.

    Article  Google Scholar 

Download references

Acknowledgements

This research was financially supported by the following organizations: the Major Science and Technology Projects of Shandong—Light weight Vehicle Body Technology of 600 km/h High Speed Maglev Train (2019TSLH0301).

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Authors and Affiliations

  1. College of Material Science and Technology, Ocean University of China, Qingdao, People’s Republic of China

    Fangyuan Wang, Zongyu Chang, Kai ** & Qiye Song

  2. State-Owned Wuhu Machinery Factory, Wuhu, People’s Republic of China

    Shanyong Xuan

  3. CRRC Sifang Vehicle Co., Ltd., Qingdao, People’s Republic of China

    Yulong Gao

  4. College of Materials Science and Technology, Nan**g University of Aeronautics and Astronautics, Nan**g, People’s Republic of China

    Hao Wang

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  1. Fangyuan Wang
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Correspondence to Kai ** or Yulong Gao.

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Wang, F., Xuan, S., Chang, Z. et al. Effect of Grinding Parameters on Industrial Robot Grinding of CFRP and Defect Formation Mechanism. Int. J. of Precis. Eng. and Manuf.-Green Tech. 11, 427–438 (2024). https://doi.org/10.1007/s40684-023-00561-0

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