Besl, P. J., & McKay, N. D. (1992). Method for registration of 3-D shapes. In Sensor fusion IV: control paradigms and data structures (Vol. 1611, pp. 586–606). Spie.
Decker, N., Wang, Y., & Huang, Q. (2020). Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling. Journal of Manufacturing Systems,56, 587–597. https://doi.org/10.1016/j.jmsy.2020.04.001 Article Google Scholar
Ghorbani, H., & Khameneifar, F. (2022). Construction of damage-free digital twin of damaged aero-engine blades for repair volume generation in remanufacturing. Robotics and Computer-Integrated Manufacturing,77, 102335. https://doi.org/10.1016/j.rcim.2022.102335 Article Google Scholar
Greenspan, M., & Yurick, M. (2003). Approximate kd tree search for efficient ICP. In Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings. (pp. 442–448). IEEE. https://doi.org/10.1109/IM.2003.1240280.
Guo, Y., Huang, L., Liu, Y., Liu, J., & Wang, G. (2020). Establishment of the complete closed mesh model of rail-surface scratch data for online repair. Sensors,20(17), 4736. https://doi.org/10.3390/s20174736 Article Google Scholar
Han, L., Xu, C., Zhongwei, L., Kai, Z., Yusheng, S., & Hao, J. (2018) A robot-driven 3D shape measurement system for automatic quality inspection of thermal objects on a forging production line. Sensors, 18(12), 4368. https://doi.org/10.3390/s18124368.
Hassan, M., Svadling, M., & Björsell, N. (2023). Experience from implementing digital twins for maintenance in industrial processes. Journal of Intelligent Manufacturing, pp 1–10. https://doi.org/10.1007/s10845-023-02078-4.
Jia, D., Zhang, W., Wang, Y., & Liu, Y. (2021). A new approach for cylindrical steel structure deformation monitoring by dense point clouds. Remote Sensing,13(12), 2263. https://doi.org/10.3390/rs13122263 Article Google Scholar
Li, T., Gao, L., Li, P., & Pan, Q. (2016). An ensemble fruit fly optimization algorithm for solving range image registration to improve quality inspection of free-form surface parts. Information Sciences,367, 953–974. https://doi.org/10.1016/j.ins.2016.07.030 Article Google Scholar
Li, L., Li, C., Tang, Y., & Du, Y. (2017). An integrated approach of reverse engineering aided remanufacturing process for worn components. Robotics and Computer-Integrated Manufacturing,48, 39–50. https://doi.org/10.1016/j.rcim.2017.02.004 Article Google Scholar
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., & Chen, B. (2018b). Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems, 31.
Li, P., Cheng, K., Jiang, P., & Katchasuwanmanee, K. (2022a). Investigation on industrial dataspace for advanced machining workshops: Enabling machining operations control with domain knowledge and application case studies. Journal of Intelligent Manufacturing,33, 103–119. https://doi.org/10.1007/s10845-020-01646-2 Article Google Scholar
Li, B., Zhang, Y., & Sun, F. (2022b). Deep residual neural network based PointNet for 3D object part segmentation. Multimedia Tools and Applications, 1–15. https://doi.org/10.1007/s11042-020-09609-8.
Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017a). Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 652–660). https://doi.org/10.1109/cvpr.2017.16.
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017b). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing systems, 30.
Qi, T. F., Fang, H. R., Chen, Y. F., & He, L. T. (2023). Research on digital twin monitoring system for large complex surface machining. Journal of Intelligent Manufacturing, pp 1–14. https://doi.org/10.1007/s10845-022-020722.
Rusu, R. B., Blodow, N., & Beetz, M. (2009). Fast point feature histograms (FPFH) for 3D registration. In 2009 IEEE international conference on robotics and automation (pp. 3212–3217). IEEE. https://doi.org/10.1109/ROBOT.2009.5152473.
Shahzad, M., & Zhu, X. X. (2014). Robust reconstruction of building facades for large areas using spaceborne TomoSAR point clouds. IEEE Transactions on Geoscience and Remote Sensing,53(2), 752–769. https://doi.org/10.1109/TGRS.2014.2327391 Article Google Scholar
Stan, L., Nicolescu, A. F., Pupăză, C., & Jiga, G. (2022). Digital Twin and web services for robotic deburring in intelligent manufacturing. Journal of Intelligent Manufacturing, pp 1–17. https://doi.org/10.1007/s10845-022-01928-x.
Wang, J., Xu, C., Dai, L., Zhang, J., & Zhong, R. (2020). An unequal deep learning approach for 3-D point cloud segmentation. IEEE Transactions on Industrial Informatics,17(12), 7913–7922. https://doi.org/10.1109/TII.2020.3044106 Article Google Scholar
Zhang, X., Li, W., Adkison, K. M., & Liou, F. (2018). Damage reconstruction from tri-dexel data for laser-aided repairing of metallic components. The International Journal of Advanced Manufacturing Technology,96, 3377–3390. https://doi.org/10.1007/s00170-018-1830-3 Article Google Scholar
Zhou, G., Zhou, K., Zhang, J., Yuan, M., Wang, X., Feng, P., Zhang, M., & Feng, F. (2022). Digital modeling-driven chatter suppression for thin-walled part manufacturing. Journal of Intelligent Manufacturing, pp 1–17. https://doi.org/10.1007/s10845-022-02045-5.
Zhu, L., Yan, B., Wang, Y., et al. (2021). Inspection of blade profile and machining deviation analysis based on sample points optimization and NURBS knot insertion. Thin-Walled Structures,162, 107540. https://doi.org/10.1016/j.tws.2021.107540 Article Google Scholar