Shengqiang Zhao

According to our database1, Shengqiang Zhao authored at least 10 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Robotic milling posture adjustment under composite constraints: A weight-sequence identification and optimization strategy.
Robotics Comput. Integr. Manuf., February, 2024

In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach.
J. Intell. Manuf., January, 2024

A sparse knowledge embedded configuration optimization method for robotic machining system toward improving machining quality.
Robotics Comput. Integr. Manuf., 2024

2022
A deep transfer regression method based on seed replacement considering balanced domain adaptation.
Eng. Appl. Artif. Intell., 2022

A Knowledge-Embedded End-to-End Intelligent Reasoning Method for Processing Quality of Shaft Parts.
Proceedings of the Intelligent Robotics and Applications - 15th International Conference, 2022

A foreknowledge perception method of multi-stages machining accuracy in aviation turbine shafts based on hidden Markov model and Neural networks<sup>*</sup>.
Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2022

Analysis and inference of stream of dimensional errors in multistage machining process based on an improved semiparametric model.
Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2022

2021
Tool wear parameters identification in precision milling using a hybrid model combining cutting forces analytical model and Gaussian process regression method.
Proceedings of the 27th International Conference on Mechatronics and Machine Vision in Practice, 2021

A transfer learning based geometric position-driven machining error prediction method for different working conditions.
Proceedings of the 27th International Conference on Mechatronics and Machine Vision in Practice, 2021

A hybrid mechanism-based and data-driven approach for the calibration of physical properties of Ni-based superalloy GH3128.
Proceedings of the 27th International Conference on Mechatronics and Machine Vision in Practice, 2021


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