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- 저자 : Pengliang Zhou (검색결과 3 건)
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Huan Yan,Peitang Wei,Pengliang Zhou,Ling Chen,Huaiju Liu,Caichao Zhu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.3
Tooth bending fatigue remains as a fundamental bottleneck restricting the safety and reliability of modern high-performance gear. In this study, a serial of bending fatigue tests on case carburized and shot peening treated gears is conducted. The fatigue crack propagation behaviors during bending fatigue test were analyzed. The alternations of macroscopic mechanical properties and microstructure features of gear samples subjected to bending fatigue are experimentally examined and analyzed in detail. It is found that the measured tooth root crack trajectories basically conform to the dangerous section of the tooth root determined by the 30° tangent method with a slight variation of about 1°. The phenomena of residual stress relaxation in gear bending fatigue is observed with the maximum residual compressive stress decreasing from -698 MPa of intact state to -572 MPa of failure state. Furthermore, the content of retained austenite of tooth root surface decreases by about 3.8 % after the gear bending fatigue failure, and the grain size of the tooth root core region illustrates a certain tendency of coarsening.
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Wu Hanlin,Wei Peitang,Hu Rui,Liu Huaiju,Du Xuesong,Zhou Pengliang,Zhu Caichao 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1
Correlation between machining errors and transmission accuracy of planetary roller screw mechanism (PRSM) plays an important role in tolerance design. In this study, analytical calculations, machine learning, and experimental verification are utilized for exploring the internal correlation between the machining errors and the transmission accuracy of the PRSM. A multi-roller meshing transmission error model is established, which comprehensively considers the eccentric error, nominal diameter error, flank angle error, and cumulative pitch error of the screw, roller, and nut. The importance coefficients of various machining errors on the transmission error are determined using the random forest algorithm. A genetic algorithm-back propagation neural network algorithm-based method is utilized for training the dataset generated via analytical calculations. The results show that the proposed analytical calculation model reflects the alternate meshing characteristics of rollers during the PRSM motion, providing a more accurate prediction of the transmission error than the existing prediction methods. For an actual mean travel deviation, the most significant machining error is the cumulative pitch error of the screw, whereas for the actual bandwidth of useful travel, the most significant machining errors are the eccentric errors of the screw and nut. The proposed prediction formulae for transmission error considering the essential machining errors illustrate reasonable prediction accuracy, with an average error of 10.63% for the actual mean travel deviation and 14.27% for the actual bandwidth of useful travel compared with the experiments, which can effectively support the direct design of PRSM tolerance in engineering practice.
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Liu Genshen,Wei Peitang,Liu Huaiju,Du Xuesong,Hu Rui,He Huilin,Zhou Pengliang,Tan Xiaoqing 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
The planetary roller screw mechanism (PRSM) is an advanced linear transmission device. The relationship between tolerance allocation and performance risk still remains elusive, which is a challenge for its future applications. This work proposes a novel transmission accuracy–axial backlash–fatigue life-driven tolerance optimization method for the screw, roller, and nut of PRSM. A computational framework for PRSM transmission accuracy, axial backlash, and fatigue life calculation is developed to work on the parametric variation of design parameters including the eccentric, pitch, nominal diameter, and flank angle. Combinations of parametric variation are obtained by the Latin hypercube sampling-based tolerance statistical model to rapidly evaluate PRSM performance risk under operation conditions and tolerance parameters. The optimal tolerance parameters with the expanded width of tolerance interval and the minimum PRSM performance risk probability are obtained using the non-dominated sorting genetic algorithm. Results reveal that PRSM performance risk probabilities change from 89.25 to 68.72% and 58.1 to 56.86%, with 29.94 and 17.38% tolerance interval width increase under the high-precision and heavy-loading operation cases studied, respectively.
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