Uncertainty analysis and reliability improvement of planetary roller screw mechanism using active learning Kriging model. (April 2023)

• Record Type: Journal Article
• Authors: Yao, Qin; Zhang, Mengchuang; Jiang, Quansheng; Ma, Shangjun
• Is Part Of: Probabilistic engineering mechanics. Volume 72(2023)
• Publication Date: 2023-04
• Subjects: Planetary roller screw mechanism -- Uncertainty analysis -- Quasi-Monte Carlo -- Active learning Kriging; Engineering -- Statistical methods -- Periodicals; Mechanics, Applied -- Statistical methods -- Periodicals; Probabilities -- Periodicals; Ingénierie -- Méthodes statistiques -- Périodiques; Mécanique appliquée -- Méthodes statistiques -- Périodiques; Probabilités -- Périodiques; 620.100727
• DOI: 10.1016/j.probengmech.2023.103436
• Languages: English
• ISSNs: 0266-8920

Abstract

Abstract: The uncertainties in the geometry, material and operation conditions may cause structural failure of the planetary roller screw mechanism (PRSM). The uncertainty analysis model is the key to the reliability assessment of the PRSM, however, the relevant studies have been rarely reported in the past. This paper focuses on establishing a preliminary mathematical model of the PRSM considering uncertain factors. The quasi-Monte Carlo (QMC) method is introduced to improve the solving efficiency of the multidimensional and nonlinear implicit limit state function (LSF). Then, the parameter sensitivities of the uncertain factors to the load distribution and contact characteristics are comprehensively ranked by the design of experiment (DoE). The computational cost for constructing the active learning Kriging (ALK) model of PRSM is decreased by only selecting the most sensitive variables. Moreover, the ALK model and QMC method (ALK-QMC) are combined to explore how the main factors affect the structural reliability of PRSM, which further guides the implementation of multi-objective optimization to improve the reliability by the developed NSGA-II-Downhill algorithm. Finally, the theoretical model and optimization results are verified by the finite element method.