Improved mesh stiffness calculation model of comprehensive modification gears considering actual manufacturing

2022 ◽  
Vol 167 ◽  
pp. 104470
Author(s):  
Zhou Sun ◽  
Siyu Chen ◽  
Zehua Hu ◽  
Xuan Tao
Keyword(s):  
Calculation Model ◽  
Mesh Stiffness

Vibration-based fault diagnosis of helical gear pair with tooth surface wear considering time-varying friction coefficient under mixed EHL condition

2021 ◽  
pp. 1-16
Author(s):  
Siyu Wang ◽  
Rupeng Zhu
Keyword(s):  
Dynamic Response ◽  
Hydrodynamic Lubrication ◽  
Helical Gear ◽  
Calculation Model ◽  
Tooth Surface ◽  
Time Varying ◽  
Gear Pair ◽  
Surface Wear ◽  
Mesh Stiffness ◽  
Helical Gear Pair

Abstract Based on “slice method”, the improved time-varying mesh stiffness (TVMS) calculation model of helical gear pair with tooth surface wear is proposed, in which the effect of friction force that obtained under mixed elasto-hydrodynamic lubrication (EHL) is considered in the model. Based on the improved TVMS calculation model, the dynamic model of helical gear system is established, then the influence of tooth wear parameters on the dynamic response is studied. The results illustrate that the varying reduction extents of mesh stiffness along tooth profile under tooth surface wear, in addition, the dynamic response in time-domain and frequency-domain present significant decline in amplitude under deteriorating wear condition.

Analytical calculation models for mesh stiffness and backlash of spur gears under temperature effects

2021 ◽  
pp. 095440622110498
Author(s):  
Ruxin Lu ◽  
Wencheng Tang
Keyword(s):  
Analytical Calculation ◽  
Calculation Model ◽  
Tooth Profile ◽  
Thermal Time ◽  
Time Varying ◽  
Mesh Stiffness ◽  
Profile Error ◽  
Gear Teeth ◽  
Influencing Mechanism ◽  
Torque Load

The temperature has a great contribution to the mesh stiffness and backlash of the gear pair. Presence of thermal deformation caused by temperature will complicate the gear teeth interaction. In this paper, the thermal time-varying stiffness model and thermal time-varying backlash model are proposed with the consideration of tooth profile error and total thermo-elastic deformation consists of the teeth deformation, teeth contact deformation, and gear body-induced deformation. The key parameters of thermo-elastic coupling deformation affected by temperature are calculated. Based on the proposed models, the influencing mechanism of temperature on the tooth profile error, mesh stiffness, total deformation, and backlash are revealed. The effects of shaft radius and torque load on the thermal stiffness and thermal backlash are studied. The proposed thermal stiffness and backlash calculation model are proven to be more comprehensive and the correctness is validated.

scholarly journals Dynamic Characteristic Analysis of Spur Gear System Considering Tooth Contact State Caused by Shaft Misalignment

2021 ◽  
Author(s):  
Lantao Yang ◽  
Qiang Zeng ◽  
Haishi Yang ◽  
Liming Wang ◽  
Guorong Long ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
Calculation Model ◽  
Gear System ◽  
Characteristic Analysis ◽  
Mesh Stiffness ◽  
Tooth Contact ◽  
Contact State ◽  
Shaft Misalignment ◽  
State Changes ◽  
Gear Contact

Abstract Shaft misalignment will change the gear contact state, and then leads to the variation of the internal stiffness excitation of the gear pair, and finally the dynamic characteristics of the gear system will be affected. However, the influence of the gear contact state change on stiffness is usually neglected in the traditional stiffness calculation model for misaligned gears, and the underlying influence mechanism of the gear contact state changes aroused by the shaft misalignment on the dynamic characteristics of gear system is still unclear. To address these shortcomings, traditional loaded tooth contact analysis (LTCA) model is improved with the influences of fillet foundation deformation taken into consideration. Combined with the improved LTCA model, a new mesh stiffness calculation model for misaligned gear considering the tooth contact state is proposed, and then the effects of the contact state changes aroused by the shaft misalignment on the mesh stiffness excitation are studied. Moreover, a dynamic model of misaligned gear system with 8 degree of freedom (DOF) is established, and the dynamic characteristics of the system are simulated and finally verified by experiment. The results show that the proposed model can be used to evaluate the dynamic characteristics of the misaligned gear system with the change of gear tooth contact state taken into consideration. This study provides a theoretical method for the evaluation and identification of the shaft misalignment error.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Calculation model of formation of nonmetallic inclusions of multilayer morphology in weld metal

2018 ◽  
Vol 2018 (2) ◽  
pp. 5-10
Author(s):  
L.A. Taraborkin ◽  
◽  
V.V. Golovko ◽  
Keyword(s):  
Weld Metal ◽  
Nonmetallic Inclusions ◽  
Calculation Model

Development of the Partial Discharge Inception Calculation Model in a Non-uniform Air Gap Considering the Effect of Humidity

2011 ◽  
Vol 131 (12) ◽  
pp. 1017-1023 ◽  
Author(s):  
Norihito Yanagita ◽  
Tatsuro Kato ◽  
Toshiaki Rokunohe ◽  
Takeshi Iwata ◽  
Hiroki Kojima ◽  
...  
Keyword(s):  
Partial Discharge ◽  
Calculation Model ◽  
Air Gap

A Calculation Model for Submicron Field-Effect Transistors Based on GaAs

2002 ◽  
Vol 58 (7-8) ◽  
pp. 9
Author(s):  
S. A. Zuev ◽  
V. V. Starostenko ◽  
A. A. Shadrin
Keyword(s):  
Field Effect ◽  
Field Effect Transistors ◽  
Calculation Model

Regression analysis of indicators of a numerical experiment to determine the cooling time of oil

2020 ◽  
pp. 89-97
Author(s):  
A. U. Yakupov ◽  
D. A. Cherentsov ◽  
K. S. Voronin ◽  
Yu. D. Zemenkov
Keyword(s):  
Regression Analysis ◽  
Computer Experiment ◽  
Calculation Model ◽  
Cooling Time ◽  
Regression Coefficients ◽  
Fisher Criterion ◽  
Empirical Relationships ◽  
Factors Affecting ◽  
Oil Pipeline ◽  
Significant Regression

The article performed the processing of the results of a computer experiment to determine the cooling time of oil in a stopped oil pipeline. We proposed a calculation model in previous works that allows you to simulate the process of cooling oil.There was a need to verify the previously obtained results when conducting a laboratory experiment on a stand with soil. To conduct the experiment, it was necessary to conduct the planning of the experiment. The factors affecting the cooling time of oil in the oil pipeline, which will vary in the proposed experiment, are determined, empirical relationships are established. A regression analysis was carried out, and the dispersion homogeneity was checked using the Cochren criterion. The estimates of reproducibility variances are calculated. The adequacy hypothesis was tested using the Fisher criterion. Significant regression coefficients are established.

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