Paper Info
Title
Digital twin modeling for loaded contact pattern-based grinding of spiral bevel gears
Abstract
To distinguish with the conventional tooth flank grinding only considering geometric accuracy, an innovative digital twin modeling is proposed for loaded contact pattern based grinding of spiral bevel gears. Where, data-driven grinding simulation, sensitivity analysis strategy, adaptive decision and control are developed. Focusing on loaded contact pattern optimization, numerical loaded tooth contact analysis (NLTCA) considering noncentrosymmetric problem and tooth flank roughness is developed for data-driven relationship establishment. Then, an adaptive data-driven tooth flank grinding decision and control model is established. Where, the universal motion concept (UMC) machine settings is selected as the optimal design variable. It is actually an infinite approximation to the target tooth flank in form of an adaptive control system. Moreover, with point-to-point material removal distribution, the different optimization strategies are proposed for accurate tooth flank grinding. In particular, the overcutting problem on the tooth flank grinding programming is investigated. Finally, Levenberg-Marquardt method is applied to solve the established nonlinear lease square model for the accurate machine tool settings having modification variations. Thus, this accurate data-driven digital twin modeling can achieve loaded contact pattern-based grinding. The provided numerical and test instances can verify the proposed digital twin modeling.
Year
DOI
Venue
2021
10.1016/j.aei.2021.101305
Advanced Engineering Informatics
Keywords
DocType
Volume
Tooth flank grinding,Spiral bevel gears,Digital twin modeling,Loaded contact pattern,Adaptive decision and control
Journal
49
ISSN
Citations 
PageRank 
1474-0346
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Kaibin Rong100.68
Han Ding249978.16
Xiannian Kong300.34
Rong Huang400.34
Jinyuan Tang500.34