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Licensed Unlicensed Requires Authentication Published by De Gruyter May 17, 2022

Research on the application of 22Na radiolocation detection technology in advanced manufacturing process control

Siming Guo, Jun Zhang, Lei Shi, Qingwen Chen and Wang Kun Chen
From the journal Kerntechnik

Abstract

This article mainly studies the positioning function of radioactive detection technology in process control for processing devices. The accuracy of 22Na detection is not limited by the spatial area by comparing different illumination scenarios; the accuracy of inspection is independent of the accuracy of machining equipment; the accuracy of the detection is not affected by the conditions of the processed body. This study is of great significance for the future radioactive detection technology to make up for the lack of precision caused by the existing sensor technology on the spatial positioning of the processing device, the illumination environment and the material characteristics of the processed body, and for the process control research in the field of advanced manufacturing.


Corresponding author: Jun Zhang, Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou University, Binzhou, Shandong 256603, China; and Department of Engineering Technology Management, International College, Krirk University, Bangkok 10220, Thailand, E-mail:

Funding source: Research Start-Up Fund Projects for Doctoral Staff of Binzhou University

Award Identifier / Grant number: 2020Y22

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was funded by Research Start-Up Fund Projects for Doctoral Staff of Binzhou University (2020Y22).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Bagheri, R., Yousefi, A., and Shirmardi, S.P. (2020). Study on gamma-ray attenuation characteristics of some amino acids for 133Ba, 137Cs, and 60Co sources. Nucl. Sci. Tech. 31: e19, https://doi.org/10.1007/s41365-020-0725-9.Search in Google Scholar

Bi, Q., Huang, N., Sun, C., Wang, Y., Zhu, L., and Ding, H. (2015). Identification and compensation of geometric errors of rotary axes on five-axis machine by on-machine measurement. Int. J. Mach. Tool Manufact. 89: 182–191, https://doi.org/10.1016/j.ijmachtools.2014.11.008.Search in Google Scholar

Büyükyıldız, M. (2018). Effect of current intensity on radiological properties of joined 304L stainless steels for photon interaction. Nucl. Sci. Tech. 29: 72–78.10.1007/s41365-017-0353-1Search in Google Scholar

Chen, D., Dong, L., Bian, Y., and Fan, J. (2015). Prediction and identification of rotary axes error of non-orthogonal five-axis machine tool. Int. J. Mach. Tool Manufact. 94: 74–87, https://doi.org/10.1016/j.ijmachtools.2015.03.010.Search in Google Scholar

Cheng, C., Jia, W., Hei, D., Geng, S., Wang, H., and Xing, L. (2018). Determination of thickness of wax deposition in oil pipelines using gamma-ray transmission method. Nucl. Sci. Tech. 29: 19–23, https://doi.org/10.1007/s41365-018-0447-4.Search in Google Scholar

Hu, C., Bai, X., Tang, M., Tang, X., and Zhao, Z. (2019). Non-destructive measurement of magnetic properties of claw pole. Chin. J. Mech. Eng. 32: 33–43, https://doi.org/10.1186/s10033-019-0395-x.Search in Google Scholar

Huai, S., Zhang, S., Wang, X., and Zhang, J. (2020). A novel adaptive noise resistance method used for AIS real-time signal detection. Chin. J. Electron. 29: 327–336, https://doi.org/10.1049/cje.2020.01.011.Search in Google Scholar

Lawrence, E.M. (2016). Frontiers of 3D printing/additive manufacturing: from human organs to aircraft fabrication. J. Mater. Sci. Technol. 32: 987–995.10.1016/j.jmst.2016.08.011Search in Google Scholar

Lee, J.H., Liu, Y., and Yang, S.-H. (2006). Accuracy improvement of miniaturized machine tool: geometric error modeling and compensation. Int. J. Mach. Tool Manufact. 46: 1508–1516, https://doi.org/10.1016/j.ijmachtools.2005.09.004.Search in Google Scholar

Li, Y. (2015). Transformation of the mould level control system of PZH steel 1# conticaster. Sichuan Metall. 37: 36–40.Search in Google Scholar

Lin, X., Zhang, X., He, L., and Zheng, W. (2020). Multiple emitters localization by UAV with nested linear array: system scheme and 2D-DOA estimation algorithm. China Commun. 17: 117–130, https://doi.org/10.23919/jcc.2020.03.010.Search in Google Scholar

Ma, Y., Yu, M., Li, L., Ma, H., Wang, Z., and Li, J. (2015). Reversible addition-fragmentation chain transfer graft polymerization of acrylonitrile onto PE/PET composite fiber initiated by γ-irradiation. Nucl. Sci. Tech. 26: 36–41.Search in Google Scholar

Shi, R., Tuo, X., Li, H., Xu, Y., Shi, F., Yang, J., and Luo, Y. (2017). Unfolding analysis of LaBr3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration. Nucl. Sci. Tech. 29: 10–18, https://doi.org/10.1007/s41365-017-0340-6.Search in Google Scholar

Srinivasa Prasad, B., Siva Prasad, D., Sandeep, A., and Veeraiah, G. (2013). Condition monitoring of CNC machining using adaptive control. Int. J. Autom. Comput. 10: 202–209, https://doi.org/10.1007/s11633-013-0713-1.Search in Google Scholar

Wang, Y., Chen, H., Chen, F., Yuan, Y., and Peng, T. (2018). Radiation dose detection using a high-power portable optically stimulated luminescence real-time reading system. Nucl. Sci. Tech. 29: e149, https://doi.org/10.1007/s41365-018-0484-z.Search in Google Scholar

Wu, D., Wang, H., Peng, J., Zhang, K., Yu, J., Zheng, X., and Chen, Y. (2020). Machining fixture for adaptive CNC machining process of near-net-shaped jet engine blade. Chin. J. Aeronaut. 33: 1311–1328, https://doi.org/10.1016/j.cja.2019.06.008.Search in Google Scholar

Yang, H., Xin, L., Wu, X., Huang, C., and Huang, W. (2011). Laser solid forming repairing and remanufacturing of high strength steel. Rare Met. Mater. Eng. 40: 148–151.Search in Google Scholar

Zhang, W., Niu, F., Wu, Y., and Guo, Z. (2019). Feasibility analysis of 60Co production in pressurized water reactors. Nucl. Sci. Tech. 30: 107–115, https://doi.org/10.1007/s41365-019-0680-5.Search in Google Scholar

Zheng, H., Tuo, X., Peng, S., Shi, R., Li, H., Lu, J., and Li, J. (2018). Determination of gamma point source efficiency based on a back-propagation neural network. Nucl. Sci. Tech. 29: 79–87, https://doi.org/10.1007/s41365-018-0410-4.Search in Google Scholar

Received: 2021-10-01
Published Online: 2022-05-17
Published in Print: 2022-06-27

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