Abstract
This paper presents an optimization method based on the Kriging and multi-objective genetic algorithm. First, taking the hinged beam structure of the cubic diamond press as the design object, the optimization design mathematical model was established with the mass as the objective function, the stress and displacement peak as the constraint conditions. Second, in combination with SolidWorks and ANSYS Workbench, parametric modeling analysis was conducted to obtain a large number of sample points sparing less time, and the agent model constructed by Kriging was trained and verified. Finally, taking advantage of global search of the multi-objective genetic algorithm, a lightweight design was realized and the mass of the hinge beam structure was effectively reduced, which would be a guiding significance for the lightweight design of other mechanical parts.
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Abbreviations
- π :
-
Correlation coefficient
- d i :
-
The ranking difference of the two variables
- E :
-
Elastic modulus
- ρ :
-
Poisson’s ratio
- μ s :
-
Yield strength
- δ :
-
Allowable displacement
- \({\hat y}\) :
-
Predicted response value
- y i :
-
Actual response value
- \({\overline y _i}\) :
-
Average value of the actual response value
- R ij :
-
The correlation function matrix
- \({\hat \mu }\) :
-
Predicted value
- \({{\hat \sigma }^2}\) :
-
Model prediction variance
- STD :
-
Standard deviation of the test sample set
- R 2 :
-
Coefficient of determination
- RMSE :
-
The root mean square error
- RMAE :
-
The relative maximum absolute error
- RMAE :
-
The relative mean absolute error
- i d :
-
The crowding degree of the individuals
References
F. Xiaohu, W. Jianjie and Y. Ye, Rapid development of large-scale cubic hinge press and relevant issues in China, Superhard Material Engineering, 23(1) (2011) 42–45.
Y. Ye, W. Jianjie, L. Ying and F. Xiaohu, The new understanding of increasing the quality of diamond depend on large-size of cubic hinge apparatus, Superhard Material Engineering, 25(6) (2013) 15–19.
T. A. Abu Shreehah and U. F. Al-Qawabeha, Diamond pressing versus shot peening, International Journal of Microstructure and Materials Properties, 5(6) (2010) 524–535.
D. Libin, X. Ningcong, H. Zhaohui, L. Guangyao and C. Aiguo, An efficient lightweight design strategy for body-in-white based on implicit parameterization technique, Structural and Multidis-ciplinary Optimization, 55(5) (2017) 1927–1943.
L. Shihao, D. Yanbin and L. Mao, Study on lightweight structural optimization design system for gantry machine tool, Concurrent Engineering, 27(2) (2019) 170–185.
S. Z. Feng, Y. R. Tao, Z. J. Ma, G. Królczyk and Z. X. Li, Transient nonlinear heat transfer analysis using a generic grid refinement for structure parameter variations, International Journal of Thermal Sciences, 153(C) (2020) 106357.
F. Xu, J. Wang and L. Hua, Multi-objective biomimetic optimization design of stiffeners for automotive door based on vein unit of dragonfly wing, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (2021).
R. W. Blanning, The construction and implementation of metamodels, Simulation, 24(6) (1975) 177–184.
W. Shuang, X. Jiefang, D. Ling and Z. Honjuan, Multi-objective optimization of microstructure of gravure cell based on response surface method, Processes, 9(2) (2021) 403.
K. Di, C. Zejun, F. Youhua, L. Cheng and T. Yinghong, Optimization on kinematic characteristics and lightweight of a camellia fruit picking machine based on the Kriging surrogate model, Mechanics and Industry, 22 (2021) 2021017.
U. Shrestha, Y. Choi, J. Park and H. Cho, Reduced-dimensional design optimization of stay vane and casing of reaction hydro turbines using global sensitivity analysis, Journal of Mechanical Science and Technology (2021) 1487–1499.
G. Carvalho José Pedro, C. R. Carvalho Érica, E. C. Vargas Dênis, H. Hallak Patrícia, S. L. P. Lima Beatriz and C. C. Lemonge Afonso, Multi-objective optimum design of truss structures using differential evolution algorithms, Computers and Structures, 252 (2021) 106544.
J. Zhiyuan, H. Xianzhen, C. Miaoxin, L. Chun and G. Yang, Thermal error prediction and reliability sensitivity analysis of motorized spindle based on Kriging model, Engineering Failure Analysis, 127 (2021) 105558.
M. Bogomolni, U. Kirsch and I. Sheinman, Efficient design sensitivities of structures subjected to dynamic loading, International Journal of Solids and Structures, 43(18–19) (2006) 5485–5500.
D. Qin, Y. Liang, L. Chen and Y. Zhong, Research for hinge sleeve’s structural optimal design based on fatigue intensity, Journal of Mechanical Strength, 28(2) (2006) 306–310.
L. Rui, X. Bojing, Z. Qingchao, G. Xue, Z. Guoliang, M. Hongan and J. Xiaopeng, Finite-element analysis on pressure transfer mechanism in large-volume cubic press, High Pressure Research, 36(4) (2016) 575–584.
M. Liang, L. Beihua and Y. Yuan, The stress analysis and tests on the hinge beam of the diamond synthesis cubic press, Matec Web of Conferences, 77 (2016) 01036.
Z. Qingping and Z. Jianhua, Lightweight design of vertical lathe bed for repairing wheel based on ANSYS Workbench, Journal of Physics: Conference Series, 1676(1) (2020) 012031.
D. Khalil and E. H. Abdelkhalak, Thermal reliability-based design optimization using Kriging model of PCM based pin fin heat sink, International Journal of Heat and Mass Transfer, 166 (2021) 120745.
Z. Jian and R. Wei, Lightweight optimization design of a light electric commercial vehicle frame, Journal of Physics: Conference Series, 1939(1) (2021) 0120387.
S. Z. Feng, X. Han, Z. X. Li and A. Incecik, Ensemble learning for remaining fatigue life prediction of structures with stochastic parameters: a data-driven approach, Applied Mathematical Modelling, 101 (2022) 420–431.
S. Z. Feng and X. Han, A novel multi-grid based reanalysis approach for efficient prediction of fatigue crack propagation, Computer Methods in Applied Mechanics and Engineering, 353 (2019) 107–122.
P. Shuai and H. Wei, Optimization design of heat pipe heat exchanger using response surface methodology, E3S Web of Conferences, 261(2) (2021) 01030.
W. Kai, O. Sigmund and D. Jianbin, Design of metamaterial mechanisms using robust topology optimization and variable linking scheme, Structural and Multidisciplinary Optimization, 63(4) (2021) 1–14.
Y. Liu, F. Xu, K. Wu, S. Zhang and J. Qiao, Multi-objective topological optimization of an electric truck frame based on orthogonal design and analytic hierarchy process, IEEE Access, 8 (2020).
X. Fengxiang, Y. Kejiong and H. Lin, In-plane dynamic response and multi-objective optimization of negative Poisson’s ratio (NPR) honeycomb structures with sinusoidal curve, Composite Structures, 269 (2021) 114018.
Z. Sekulski, Multi-objective topology and size optimization of high-speed vehicle-passenger catamaran structure by genetic algorithm, Marine Structures, 23(4) (2010) 405–433.
S. Z. Feng, Y. Xu, X. Han, Z. X. Li and A. Incecik, A phase field and deep-learning based approach for accurate prediction of structural residual useful life, Computer Methods in Applied Mechanics and Engineering, 383 (2021) 113885.
S. Z. Feng, X. Han, Z. J. Ma, G. Królczyk and Z. X. Li, Data-driven algorithm for real-time fatigue life prediction of structures with stochastic parameters, Computer Methods in Applied Mechanics and Engineering, 372 (2020) 113373.
P. Xiang, Q. Chan, L. Jiquan, W. Huaping, L. Zhenyu and J. Shaofei, Multiple-scale uncertainty optimization design of hybrid composite structures based on neural network and genetic algorithm, Composite Structures (Prepublish) (2020) 113371.
D. T. Fabio, S. A. Pio and M. G. Carlo, A new genetic algorithm-based framework for optimized design of steel-jacketing retrofitting in shear-critical and ductility-critical RC frame structures, Engineering Structures (2021) 243.
F. Jianguang, G. Yunkai, S. Guangyong and L. Qing, Multi-objective reliability-based optimization for design of a vehicle-door, Finite Elements in Analysis and Design, 67 (2013) 140923–140935.
J. F. Aguilar Madeira, H. Rodrigues and H. Pina, Multi-objective optimization of structures topology by genetic algorithms, Advances in Engineering Software, 36(1) (2003) 21–28.
S. Z. Feng, X. Han and G. Wang, An efficient on-line algorithm for the optimal design of multi-material structures under thermal loads, International Journal of Thermal Sciences, 132 (2018) 567–577.
Acknowledgments
This work was partly supported by the National Natural Science Foundation of China (11602229, and 52075500), by Key Scientific Project of Henan Province (211110220200), and by Key Scientific Research Projects of Institutions of Higher Learning in (21A460029, and 21A460030).
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Shixin Zhang received his B.S. in Mechanical Design and Automation from Zhengzhou University of Light Technology, and now is a graduate student there, majoring in mechanical structure optimization design.
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Xie, G., Zhang, S., Wang, L. et al. Lightweight design of hinge beam based on Kriging agent model. J Mech Sci Technol 36, 3585–3595 (2022). https://doi.org/10.1007/s12206-022-0634-4
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DOI: https://doi.org/10.1007/s12206-022-0634-4