Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling-Reference-Cited by-同舟云学术

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Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

Published:2021-06 Issue:6 Volume:34 Page:162-177
ISSN:1000-9361
Container-title:Chinese Journal of Aeronautics
language:en
Short-container-title:Chinese Journal of Aeronautics

Author:

CHEN Zhanying,WANG Liping,ZHANG Jiabo,GUO Guoqiang,FU Shuailei,WANG Chao,LI Xuekun

Funder

Ministry of Industry and Information Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Mechanical Engineering,Aerospace Engineering

Reference26 articles.

1. Improved forecasting compensatory control to guarantee the remaining wall thickness for pocket milling of a large thin-walled part;Wang;Int J Adv Manuf Technol,2018

2. Simulation and experimental investigation of the end milling process considering the cutter flexibility;Xu;Int J Mach Tools Manuf,2003

3. Prediction of contour accuracy in the end milling of pockets;Law;J Mater Process Technol,2001

4. Efficient algorithms for calculations of static form errors in peripheral milling;Wan;J Mater Process Technol,2006

5. Milling error prediction and compensation in machining of low-rigidity parts;Ratchev;Int J Mach Tools Manuf,2004

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1. Accurate prediction and compensation of machining error for large components with time-varying characteristics combining physical model and double deep neural networks;Journal of Manufacturing Processes;2023-08

2. Digital Engineering Methods in Practical Use during Mechatronic Design Processes;Designs;2023-07-14

3. Integrating physics-informed recurrent Gaussian process regression into instance transfer for predicting tool wear in milling process;Journal of Manufacturing Systems;2023-06

4. A novel multi-pass machining accuracy prediction method for thin-walled parts;The International Journal of Advanced Manufacturing Technology;2023-04-25

5. A novel multi-pass machining accuracy prediction method for thin-walled parts;2022-09-16

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