Digital twin modeling for tooth surface grinding considering low-risk transmission performance of non-orthogonal aviation spiral bevel gears

接触分析 螺旋锥齿轮 齿面 灵敏度(控制系统) 工程类 结构工程 刚度 传输(电信) 机械工程 有限元法 电子工程 电气工程
作者
Duncai Lei,Kaibin Rong,Biyun Song,Han Ding,Jinyuan Tang
出处
期刊:Isa Transactions [Elsevier BV]
卷期号:128: 646-663 被引量:17
标识
DOI:10.1016/j.isatra.2021.11.036
摘要

Low-risk transmission performance including elastic deformation, loaded contact pattern, load distribution and loaded transmission error is of paramount significance to the actual manufacturing for non-orthogonal aviation spiral bevel gears. The advanced digital twin technology is introduced into tooth flank grinding. A new digital twin modeling considering low-risk transmission performances is proposed. In the modeling, low-risk transmission performance driven simulation, sensitivity analysis and robust control are developed, respectively Firstly, data-driven tooth surface modeling is developed by simulating free-form tooth surface grinding including gear tilt method and pinion double helical method. With local geometric boundary setup meshing stiffness is determined by using local Rayleigh–Ritz solution. Then, to deal with the sensitivity of gear assembly, an improved tooth contact analysis (TCA) is developed. Moreover, numerical loaded tooth contact analysis (NLTCA) is performed to build a bridge between of low-risk performances and hypoid generator parameters. The low-risk transmission performance driven control model is established by using hypoid generator parameters modification. Finally, sensitivity analysis strategy-based robust control model is solved by using Levenberg–Marquardt method for accurate hypoid generator parameters having modification amount. The provided numerical instance can verify the proposed method. • The free-form tooth flank grinding for the new non-orthogonal aerospace spiral bevel gears. • NLTCA for establishing data-driven function relation. • Tooth flank geometric accuracy control by considering loaded contact deformation predication. • Optimal control strategy by selecting a small of machine settings based on sensitivity analysis. • Digital twin modeling considering both geometric and low-risk transmission performances.
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