CV

Education

• Ph.D in mechanical engineering, Huazhong University of Science and Technology, 2020~2025 (expected)
• B.S. in mechanical engineering, Nanjing University of Aeronautics and Astronautics (NUAA) , 2016~2020

Skills

• Engineering capabilities
  • Actual robot operation（Staubli，ABB，KUKA）
  • Industrial PLC（Beckhoff，Siemens）
  • Machining（Milling, Turning）
  • Additive manufacturing（Powders, fibers）
• Deep Learning
  • Deep Learning Framework（PyTorch， TensorFlow）
  • Artificial Intelligence algorithms（Machine Learning，Transfer Learning，Deep Regression Learning）
• Measurement
  • Measurement technology (point laser, line laser, surface laser and structured light)
  • PCL point cloud library
  • Laser Tracker

Publications

Digital twin-driven staged error prediction and compensation framework for the whole process of robotic machining

Zhang, T., Peng, F., Yang, Z., Tang, X., Yuan, J., & Yan, R. (2025). Digital twin-driven staged error prediction and compensation framework for the whole process of robotic machining. Journal of Manufacturing Systems, 83, 252-283, https://doi.org/10.1016/j.jmsy.2025.09.009.

UGP-KD An unsupervised generalized prediction framework for robot machining quality under historical task knowledge distillation for new tasks

T. Zhang, F. Peng, Z. Yang, X. Tang, R. Yan, UGP-KD: An unsupervised generalized prediction framework for robot machining quality under historical task knowledge distillation for new tasks, Computers in Industry, 168 (2025) 104269, https://doi.org/10.1016/j.compind.2025.104269.

An uncertainty quantification and accuracy enhancement method for deep regression prediction scenarios

Zhang, T., Peng, F., Yan, R., Tang, X., Yuan, J., & Deng, R. (2025). An uncertainty quantification and accuracy enhancement method for deep regression prediction scenarios. Mechanical Systems and Signal Processing, 227, 112394, https://doi.org/10.1016/j.ymssp.2025.112394.

Spatial–temporal feature fusion for intelligent foreknowledge of robotic machining errors

Zhang, T., Peng, F., Wang, J., Yang, Z., Tang, X., Yan, R., Zhao, S., & Deng, R. (2025). Spatial–temporal feature fusion for intelligent foreknowledge of robotic machining errors. Robotics and Computer-Integrated Manufacturing, 94, 102972, https://doi.org/10.1016/j.rcim.2025.102972.

An unsupervised prediction of robotic machining error for new tasks under historical tasks knowledge distillation

T. Zhang, F. Peng, X. Tang, Z. Yang, R. Yan, An unsupervised prediction of robotic machining error for new tasks under historical tasks knowledge distillation, Procedia CIRP, 133 (2025) 161-166, https://doi.org/10.1016/j.procir.2025.02.029.

A sparse knowledge embedded configuration optimization method for robotic machining system toward improving machining quality

T. Zhang, F. Peng, X. Tang, R. Yan, R. Deng, S. Zhao, A sparse knowledge embedded configuration optimization method for robotic machining system toward improving machining quality, Robot. Comput.-Integr. Manuf., 90 (2024) 102818, https://doi.org/10.1016/j.rcim.2024.102818.

An online prediction and compensation method for robot position errors embedded with error-motion correlation

T. Zhang, H. Sun, F. Peng, X. Tang, R. Yan, R. Deng, An online prediction and compensation method for robot position errors embedded with error-motion correlation, Measurement, (2024) 114866, https://doi.org/10.1016/j.measurement.2024.114866.

Quantification of uncertainty in robot pose errors and calibration of reliable compensation values

T. Zhang, F. Peng, R. Yan, X. Tang, R. Deng, J. Yuan, Quantification of uncertainty in robot pose errors and calibration of reliable compensation values, Robot. Comput.-Integr. Manuf., 89 (2024) 102765, https://doi.org/10.1016/j.rcim.2024.102765.

An effective robotic processing errors prediction method considering temporal characteristics

R. DENG, X. TANG, T. ZHANG*, F. PENG, J. YUAN, R. YAN, An effective robotic processing errors prediction method considering temporal characteristics, Journal of Advanced Manufacturing Science and Technology, 0 (2024) 0, https://doi.org/10.51393/j.jamst.2024010.

An active semi-supervised transfer learning method for robot pose error prediction and compensation

T. Zhang, F. Peng, X. Tang, R. Yan, C. Zhang, R. Deng, An active semi-supervised transfer learning method for robot pose error prediction and compensation, Eng. Appl. Artif. Intell., 128 (2024) 107476, https://doi.org/10.1016/j.engappai.2023.107476.

CME-EPC A coarse-mechanism embedded error prediction and compensation framework for robot multi-condition tasks

T. Zhang, F. Peng, X. Tang, R. Yan, R. Deng, CME-EPC: A coarse-mechanism embedded error prediction and compensation framework for robot multi-condition tasks, Robot. Comput.-Integr. Manuf., 86 (2024) 102675, https://doi.org/10.1016/j.rcim.2023.102675.

A deep transfer regression method based on seed replacement considering balanced domain adaptation

T. Zhang, H. Sun, F. Peng, S. Zhao, R. Yan, A deep transfer regression method based on seed replacement considering balanced domain adaptation, Eng. Appl. Artif. Intell., 115 (2022) 105238, https://doi.org/10.1016/j.engappai.2022.105238.

A transfer learning based geometric position-driven machining error prediction method for different working conditions

T. Zhang, H. Sun, L. Zhou, S. Zhao, F. Peng and R. Yan, "A transfer learning based geometric position-driven machining error prediction method for different working conditions," 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Shanghai, China, 2021, pp. 145-150, doi: 10.1109/M2VIP49856.2021.9665105.

A Knowledge-Embedded End-to-End Intelligent Reasoning Method for Processing Quality of Shaft Parts

T. Zhang, B. Li, H. Sun, S. Zhao, F. Peng, L. Zhou, R. Yan. (2022). A Knowledge-Embedded End-to-End Intelligent Reasoning Method for Processing Quality of Shaft Parts. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_39

Talks

CIRP CMMO 2025 — An unsupervised prediction of robotic machining error for new tasks under historical tasks knowledge distillation

May 20, 2025

Oral report at 20th ClRP Conference on Modeling of Machining Operations, (CIRP CMMO 2025), Mons, Belgium

ICIFT 2025 — MOSS: An integrated system for robotic machining error Measurement, Optimization, Sensing and Supervision enabled by cyber-physical systems

April 25, 2025

Oral report at The 13 th International Conference on Frontiers of Intelligent Technology, (ICIFT 2025), Chendu, China

NCIM 2024 — Intelligent technology in error sensing of robotic machining systems

August 03, 2024

Oral report at The 9-th National Academic Conference on Intelligent Manufacturing, (NCIM,2024), Guiyang, China

ICIRA 2023 — High-Precision Point Cloud Data Acquisition for Robot Based on Multiple Constraints

July 06, 2023

Oral report at The 16th International Conference on Intelligent Robotics and Applications, (ICIRA,2023), Hangzhou, China

ICIRA 2022 — A knowledge-embedded end-to-end intelligent reasoning method for processing quality of shaft parts

August 01, 2022

Oral report at The 15th International Conference on Intelligent Robotics and Applications, (ICIRA,2022), Harbin, China

M2VIP 2021 — A transfer learning based geometric position-driven machining error prediction method for different working conditions

November 27, 2021

Oral report at 27 th International Conference on Mechatronics and Machine Vision in Practice (M2VIP 2021), Shanghai, China

Teaching

Huazhong University of Science and Technology (HUST)

Nanjing University of Aeronautics and Astronautics (NUAA)

Huairen No. 1 High School

Service and leadership

• Reviewer for journals such as J. Manuf. Syst., Reliab. Eng. Syst. Saf., CMC-Comput. Mat. Contin., Adv. Eng. Mater., Mach. Sci. Technol., and International Journal of Modeling, Simulation, and Scientific Computing.