Abstract
The positive characteristics observed in terms of compact structural sizes and high torque-transmitting capacities have allowed co-rotating twin-screw (CRTS) gear transmissions to be extensively applied to instances requiring small central distances and large power densities. In this study, a multi-objective optimization model for the quick determination of structural parameters of the CRTS gear transmission system is proposed based on the heuristic search and non-dominant sorting genetic algorithm. Results reveal that the optimized scheme with 17 design variables and 18 constrained parameters can be achieved with this model. The system reduces mass by 7.74 % from the original mass of 199.06 kg, while the safety factor variance of the transmission system decreases by 29.3 %. This method applies to other transmission systems with different structures and thus provides general theoretical support for modern gear transmission optimizations.
Similar content being viewed by others
Abbreviations
- a :
-
The lower bounds of the normal module
- a 2,3 :
-
The center distances of the second and third stages
- b :
-
The upper bounds of the normal module
- b i :
-
The tooth width of the three gear stages
- c :
-
The error of the actual installation angle
- d :
-
The center distance of two output shafts
- d fi :
-
Entropy
- d s :
-
The shaft diameter of each working shaft
- d a6 :
-
The third-stage pinion indexing circle diameters
- d shaft :
-
The diameter of the gear shaft where shaft is located
- f 1(X):
-
The weight and of each stage gear
- f 2(X):
-
The safety factor variance of each stage gear
- f(Y):
-
The value of the corresponding two interpolation points
- f(α i):
-
The value of the corresponding two interpolation points
- g(Z):
-
Function reflecting the relationship between constraints and related parameters
- i 2 :
-
The limited range of the second stage gear ratio
- I i :
-
The initialization parameter set of monte carlo sampling
- m ni :
-
Normal modules, with i = 1, 2, 3
- M :
-
The set of normal modules with a range of 2~10
- M i :
-
Mass
- Oi :
-
The set of other parameters for analytic solution
- S Hi :
-
The contact safety factor
- S Fi :
-
The bending safety factor
- St(X):
-
A series heuristic search decision function
- St i :
-
The contact safety factor
- X i :
-
The design parameter
- X :
-
The vector of all design parameter
- X 2,3 :
-
The vector of design variables involved in the second-layer and third-layer cycle
- ΣX i :
-
The total shifting coefficient
- Z i :
-
The teeth numbers
- Z p :
-
The teeth numbers range of the pinion
- z w :
-
The teeth number range of the wheel
- β i :
-
The helix angle
- φai :
-
The tooth width coefficient
References
N. Hayafusa, K. Endoh and M. Hatamoto, Driver Power Transmitting Apparatus of Twin Shaft Extruders, US Pantent 5213010 (1993).
A. Ide, K. Endoh and M. Hatamoto, Drive Transmission Apparatus for Twin-Screw Extruder, US Pantent 6298751 (2001).
W. H. Wang, R. F. Song, M. C. Guo and S. S. Liu, Analysis on compound-split configuration of power-split hybrid electric vehicle, Mechanism and Machine Theory, 78 (2014) 272–288.
G. Mantriota, Power split transmissions for wind energy systems, Mechanism and Machine Theory, 117 (2017) 160–174.
L. Xu, G. K. Kyprianidis and T. U. J. Grönstedt, Optimization study of an intercooled recuperated aero-engine, Journal of Propulsion and Power, 29 (2) (2013) 424–432.
B. Vergnes, Average shear rates in the screw elements of a corotating twin-screw extruder, Polymers, 13 (2) (2021) 304.
A. Lawal and D. M. Kalyon, Mechanisms of mixing in single and co-rotating twin-screw extruders, Polymer Engineering and Science, 35 (17) (1995) 1325–1338.
W. P. Cleary and D. M. Sinnott, Simulation of particle flows and breakage in crushers using DEM: part 1 - compression crushers, Minerals Engineering, 74 (2015) 178–197.
H. Shao, H. Jiang and H. Zhang, Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network, IEEE Transactions on Industrial Electronics, 65 (3) (2017) 2727–2736.
R. V. Rao and G. G. Waghmare, A new optimization algorithm for solving complex constrained design optimization problems, Engineering Optimization, 49 (1) (2017) 60–83.
Z. Tang, X. Hu and J. Periaux, A multi-level hybridized optimization methods coupling local search deterministic and global search evolutionary algorithms, Archives of Computational Methods in Engineering, 27 (3) (2012) 939–975.
Y. M. Xu, K. L. Li, J. T. Hu and K. Q. Li, A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues, Information Sciences, 270 (2014) 255–287.
G. Kaur and S. Arora, Chaotic whale optimization algorithm, Journal of Computational Design and Engineering, 5 (3) (2018) 275–284.
A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli and R. Tavakkoli-Moghaddam, Red deer algorithm (RDA): a new nature-inspired meta-heuristic, Soft Computing, 24 (19) (2020) 14637–14665.
H. Abderazek, D. Ferhat and A. Ivana, Adaptive mixed differential evolution algorithm for bi-objective tooth profile spur gear optimization, International Journal of Advanced Manufacturing Technology, 90 (5–8) (2017) 2063–2073.
G. S. Liu, P. T. Wei, K. R. Chen, H. J. Liu and Z. H Lu, Polymer gear contact fatigue reliability evaluation with small data set based on machine learning, Journal of Computational Design and Engineering, 9 (2) (2022) 583–597.
G. S. Liu, H. J. Liu, C. C. Zhu, T. Y. Mao and G. Hu, Design optimization of a wind turbine gear transmission based on fatigue reliability sensitivity, Frontiers of Mechanical Engineering, 16 (1) (2021) 61–79.
Y. K. Wu, H. C. Tan, J. K. Peng, H. L. Zhang and H. W. He, Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus, Applied Energy, 247 (2019) 454–466.
Y. L. Lei, L. G. Hou, Y. Fu, J. L. Hu and W. Chen, Research on vibration and noise reduction of electric bus gearbox based on multi-objective optimization, Applied Acoustics, 158 (2019) 107037.
Z. Liu, B. Hu, B. T. Huang, L. L. Lang, H. X. Guo and Y. J. Zhao, Decision optimization of low-carbon dual-channel supply chain of auto parts based on smart city architecture, Complexity, 2020 (2020) 2145951.
L. Xia and P. Breitkopf, Concurrent topology optimization design of material and structure within FE2 nonlinear multiscale analysis framework, Computer Methods in Applied Mechanics and Engineering, 278 (2014) 524–542.
Z. L. Zhu, X. L. Cai, S. H. Yi, J. L. Chen, Y. W. Dai, C. Y. Niu and Z. X. Guo, Multivalency-driven formation of te-based monolayer materials: a combined first-principles and experimental study, Physical Review Letters, 119 (10) (2017) 106101.
J. Schmidt, M. R. G. Marques, S. Botti and M. A. L. Marques, Recent advances and applications of machine learning in solid-state materials science, Npj Computational Materials, 5 (2019) 83.
A. Fredriksson, A. Forsgren and B. Hardemark, Minimax optimization for handling range and setup uncertainties in proton therapy, Medical Physics, 38 (3) (2011) 1672–1684.
A. Ziaee, A. B. Albadarin, L. Padrela, T. Femmer, E. O’Reilly and G. Walker, Spray drying of pharmaceuticals and biopharmaceuticals: critical parameters and experimental process optimization approaches, European Journal of Pharmaceutical Sciences, 127 (2019) 300–318.
M. Togacar, B. Ergen and Z. Comert, COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches, Computers in Biology and Medicine, 121 (2020) 103805.
F. R. Wang, Z. Chen and G. B. Song, Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine, Mechanical Systems and Signal Processing, 136 (2020) 106507.
X. S. Yang, Bat algorithm for multi-objective optimization, International Journal of Bio-Inspired Computation, 3 (5) (2011) 267–274.
S. Mirjalili, The ant lion optimizer, Advances in Engineering Software, 83 (2015) 80–98.
G. Dhiman and V. Kumar, Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge-Based Systems, 165 (2019) 169–196.
S. W. Kim, K. Kang, K. Yoon and D. H. Choi, Design optimization of an angular contact ball bearing for the main shaft of a grinder, Mechanism and Machine Theory, 104 (2016) 287–302.
N. Srinivas and K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2 (3) (1994) 221–248.
G. Y. Zhang, G. Q. Wang, X. F. Li and Y. P. Ren, Global optimization of reliability design for large ball mill gear transmission based on the kriging model and genetic algorithm, Mechanism and Machine Theory, 69 (2013) 321–336.
C. Choi, H. Ahn, Y. J. Park, G. H. Lee and S. C. Kim, Influence of gear tooth addendum and dedendum on the helical gear optimization considering mass, efficiency, and transmission error, Mechanism and Machine Theory, 166 (2021) 104476.
S. N. Kishore, A. V. V. Reddy and L. B. Rao, Design and optimization of spur gears in a single stage reduction gear box, Materials Today: Proceedings, 60 (3) (2022) 2010–2017.
J. Wu, H. J. Liu, P. T. Wei, Q. J. Lin and S. S. Zhou, Effect of shot peening coverage on residual stress and surface roughness of 18CrNiMo7-6 steel, International Journal of Mechanical Sciences, 183 (2020) 105785.
Y. L. Wang, Q. J. Qian, G. D. Chen, S. S. Jin and Y. Chen, Multi-objective optimization design of cycloid pin gear planetary reducer, Advances in Mechanical Engineering, 9 (9) (2017).
Z. Wang, G. He, W. Du, J. Zhou, X. F. Han, J. T. Wang, H. H. He, X. M. Guo, J. Y. Wang and Y. F. Kou, Application of parameter optimized variational mode decomposition method in fault diagnosis of gearbox, IEEE Access, 7 (2019) 44871–44882.
N. G. R. Ebenezer, S. Ramabalan and S. Navaneethasanthakumar, Advanced multi criteria optimal design of spiral bevel gear pair using NSGA - II, Jordan Journal of Mechanical and Industrial Engineering, 16 (2) (2022) 185–193.
Acknowledgments
This work is supported by the National Key R&D Program of China (Grant No. 2020YFB2008200).
Author information
Authors and Affiliations
Corresponding author
Additional information
Mingzhu Hu has been studying as a master student in the State Key Laboratory Mechanical Transmissions, Chongqing University, China, since 2021. Her research interest includes the failure mechanism, loading capacity, and multi-objective optimization of gear transmission system.
Rights and permissions
About this article
Cite this article
Hu, M., Wang, H., Wei, P. et al. Multi-objective optimization of a co-rotating twin-screw gear transmission system based on heuristic search. J Mech Sci Technol 37, 5831–5841 (2023). https://doi.org/10.1007/s12206-023-1022-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-023-1022-4