Optimization of tooth modifications for spur and helical gears using an adaptive multi-objective swarm algorithm

2019 ◽  
Vol 233 (21-22) ◽  
pp. 7292-7308 ◽  
Author(s):  
C Lagresle ◽  
M Guingand ◽  
J-P de Vaujany ◽  
B Fulleringer
Keyword(s):  
Power Transmission ◽  
Optimization Problems ◽  
Transmission Error ◽  
Particle Swarm Algorithm ◽  
Spur Gears ◽  
Optimal Solutions ◽  
Helical Gears ◽  
Metaheuristic Methods ◽  
Swarm Algorithm ◽  
Maximum Contact

Metaheuristic methods have proved to be suitable for solving complex multi-criteria optimization problems. In this paper, a modified particle swarm algorithm has been implemented in order to improve the quasi-static behavior of a power transmission gearbox, thus optimizing various objectives such as the maximum contact pressure on the gear flanks, the root-mean-square of the loaded transmission error signal, the tooth bending stress, and/or the pressure-speed factor. For narrow-faced spur gears, the comparison between optimal solutions found by the algorithm and the so-called master curve shows quite good agreements. The chosen form of the profile modifications, linear or quadratic, is then discussed. Finally, the robustness of the optimal solutions is tested to guarantee their efficiency against variable shaft misalignments.

Low excitation spur gears with variable tip diameter

2021 ◽  
Vol 263 (5) ◽  
pp. 1275-1285
Author(s):  
Joshua Götz ◽  
Sebastian Sepp ◽  
Michael Otto ◽  
Karsten Stahl
Keyword(s):  
Transmission Error ◽  
Spur Gear ◽  
Low Noise ◽  
Spur Gears ◽  
Mesh Stiffness ◽  
Helical Gears ◽  
Axial Forces ◽  
Tooth Width ◽  
Drive Trains ◽  
Static Transmission Error

One important source of noise in drive trains are transmissions. In numerous applications, it is necessary to use helical instead of spur gear stages due to increased noise requirements. Besides a superior excitation behaviour, helical gears also show additional disadvantageous effects (e.g. axial forces and tilting moments), which have to be taken into account in the design process. Thus, a low noise spur gear stage could simplify design and meet the requirements of modern mechanical drive trains. The authors explore the possibility of combining the low noise properties of helical gears with the advantageous mechanical properties of spur gears by using spur gears with variable tip diameter along the tooth width. This allows the adjustment of the total length of active lines of action at the beginning and end of contact and acts as a mesh stiffness modification. For this reason, several spur gear designs are experimentally investigated and compared with regard to their excitation behaviour. The experiments are performed on a back-to-back test rig and include quasi-static transmission error measurements under load as well as dynamic torsional vibration measurements. The results show a significant improvement of the excitation behaviour for spur gears with variable tip diameter.

Coupled Dynamic and Vibration Analysis of Beveloid Geared System

2012 ◽  
Vol 215-216 ◽  
pp. 917-920
Author(s):  
Rong Fan ◽  
Chao Sheng Song ◽  
Zhen Liu ◽  
Wen Ji Liu
Keyword(s):  
Transmission Error ◽  
Time Varying ◽  
Spur Gears ◽  
Dynamic Coupling ◽  
Nonlinear Dynamic Model ◽  
Mesh Stiffness ◽  
Hypoid Gears ◽  
Helical Gears ◽  
Beveloid Gears ◽  
Vibration Model

Dynamic modeling of beveloid gears is less developed than that of spur gears, helical gears and hypoid gears because of their complicated meshing mechanism and 3-dimsional dynamic coupling. In this study, a nonlinear systematic coupled vibration model is created considering the time-varying mesh stiffness, time-varying transmission error, time-varying rotational radius and time-varying friction coefficient. Numerical integration applying the explicite Runge-Kutta formula and the implicit direct integration is used to solve the nonlinear dynamic model. Also, the dynamic characteristics of the marine gear system are investigated.

scholarly journals Improved Fitness-Dependent Optimizer Algorithm

2020 ◽  
Author(s):  
Danial A. Muhammed ◽  
Soran AM. Saeed ◽  
Tarik A. Rashid
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Standard Test ◽  
Particle Swarm Algorithm ◽  
Test Function ◽  
Dragonfly Algorithm ◽  
Whale Optimization ◽  
Real World Applications ◽  
Swarm Algorithm ◽  
Benchmark Test Functions

<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

scholarly journals Particle Swarm Optimization as an Efficient Computational Method in order to Minimize Vibrations of Multimesh Gears Transmission

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Alexandre Carbonelli ◽  
Joël Perret-Liaudet ◽  
Emmanuel Rigaud ◽  
Alain Le Bot
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Transmission Error ◽  
Computational Method ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Helical Gears ◽  
Great Performance ◽  
Applied Torque ◽  
Static Transmission Error

The aim of this work is to present the great performance of the numerical algorithm of Particle Swarm Optimization applied to find the best teeth modifications for multimesh helical gears, which are crucial for the static transmission error (STE). Indeed, STE fluctuation is the main source of vibrations and noise radiated by the geared transmission system. The microgeometrical parameters studied for each toothed wheel are the crowning, tip reliefs and start diameters for these reliefs. Minimization of added up STE amplitudes on the idler gear of a three-gear cascade is then performed using the Particle Swarm Optimization. Finally, robustness of the solutions towards manufacturing errors and applied torque is analyzed by the Particle Swarm algorithm to access to the deterioration capacity of the tested solution.

A Kind of Adaptive Quantum Particle Swarm Algorithm Based on Phase Encoding

2012 ◽  
Vol 548 ◽  
pp. 612-616
Author(s):  
Jun Hui Pan ◽  
Hui Wang ◽  
Pan Chi Li
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Quantum Bits ◽  
Swarm Algorithm ◽  
The Relationship ◽  
Better Than ◽  
Function Extremum

To improve the optimization performance of particle swarm, an adaptive quantum particle swarm optimization algorithm is proposed. In the algorithm, the spatial position of particles is described by the phase of quantum bits, and the position mutation of particles is achieved by Pauli-Z gates. An adaptive determination method of the global-factors is proposed by studying the relationship among inertia factors, self-factors and global-factors. The experimental results demonstrate that the proposed algorithm is much better than the standard particle swarm algorithm by solving the function extremum optimization problems.

Modeling and Analysis on the Gearbox of Driven Tool Holder

2012 ◽  
Vol 251 ◽  
pp. 111-113
Author(s):  
Yan Gui ◽  
Qi Zhang
Keyword(s):  
Transmission Error ◽  
Design Stage ◽  
System Response ◽  
Cad Model ◽  
Spur Gears ◽  
Excitation Mechanism ◽  
Modeling And Analysis ◽  
Helical Gears ◽  
Static And Dynamic Analysis ◽  
Tool Holder

Various methods of calculating transmission error in spur and helical gears are used to predict T.E. at the design stage. In order to reduce the driveline noise of the noise excitation mechanism, an advanced algorithm is used to predict and optimize the TE of a gear pair and the system response of specified TE excitation is investigated for the driven tool holder. And the CAD model was then meshed in Hypermesh with designable and non-designable areas. A pair of spur gears were investigated through static and dynamic analysis in detail.

A Symbiotic Multi-Species Optimizer for Discrete Optimization

2012 ◽  
Vol 150 ◽  
pp. 8-11
Author(s):  
Ying Hui Huang ◽  
Jian Sheng Zhang
Keyword(s):  
Discrete Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Swarm Algorithm ◽  
Discrete Optimization Problems

This paper presents a discrete optimization algorithm based on a model of symbiosis, called binary symbiotic multi-species optimizer (BSMSO). BSMSO extends the dynamics of the canonical binary particle swarm algorithm (CBPSO) by adding a significant ingredient, which takes into account symbiotic co evolution between species. The BSMSO algorithm is evaluated on a number of discrete optimization problems for compared with the CBPSO algorithm. The comparisons show that on average, BSMSO outperforms the BPSOs in terms of accuracy and convergence speed on all benchmark functions.

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