Manufacturing Technology 2019, 19(6):952-958 | DOI: 10.21062/ujep/402.2019/a/1213-2489/MT/19/6/952

Research on Hydraulic System Optimization of Loader Based on GA-BP

Zhang Hua, Zhao Lei, Chen Hua
School of Electrical and Electronic Engineering, Chongqing Vocational and Technical University of Mechatronics, Bi Shan 402760, China

Aiming to study the working efficiency and stability of the loader, the hydraulic system of the loader is studied. Taking the ZL50 loader as the research carrier, the working conditions of the loader and the working principle of the hydraulic system are analysed at first. AEMSim software is used to simulate and analyse the hy-draulic system, and the necessity of using the algorithm to optimize the hydraulic system is put forward. Secondly, the mathematical model of key hydraulic system optimization is deduced, and genetic algorithm and neural net-work algorithm are used to optimize the analysis of the objective function, and the simulation results are compared and analysed again. The results show that the parameters optimized by GA and BP algorithm are better than the original parameters. Further analysis shows that the parameters optimized by GA algorithm are better than BP algorithm in smoothness.

Keywords: Loader; Hydraulic system; Simulation; Genetic Algorithm; Optimization analysis
Grants and funding:

The Chongqing Municipal Higher Education Teaching Reform Major Project in 2017 (171042).
The Industrial Robot Integration Chongqing Higher Vocational and Technical College Application Technology Promotion Center Project.

Published: December 1, 2019  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Hua Z, Lei Z, Hua C. Research on Hydraulic System Optimization of Loader Based on GA-BP. Manufacturing Technology. 2019;19(6):952-958. doi: 10.21062/ujep/402.2019/a/1213-2489/MT/19/6/952.
Download citation

References

  1. WAN YIPING, JIA JIE, SONG XUDING. (2017). Dynamics simulation and experimental study of loader working device [J]. Computer Simulation, 2017 (7)
  2. YOU YONG, SUN DONG-YE, LIU JUN-LONG. (2019). Automatic shift control strategy of hydraulic mechanical automatic transmission based on dynamic programming [J]. Journal of Mechanical Engineering, 2019, 55 (8): 106-117.
  3. LIU FUXIN, DU DEJUN. (2007). Reasonable lubrication and centralized lubrication application of wheel loader [J]. Lubrication and Sealing, 2007, 32 (7): 154-155.
  4. JIAN Z. (2018). Working Principle and Energy Saving Analysis for Fixed/Variable Displacement Hydraulic System of Loader[J]. Chinese Hydraulics & Pneumatics, 2018.
  5. PARK S H, ALAM K, JEONG Y M, et al. (2009). Modeling and simulation of hydraulic system for a wheel loader using AMESim[C]// Iccassice. IEEE, 2009.
  6. ZHOU YI, YU JIN (2008). Fluid transmission and control [M]. Beijing. Science Press. 2008.
  7. IVAN VOREL, ŠTĚPÁN JENÍČEK, JOSEF KÁŇA, VRATISLAV KOTĚŠOVEC. Optimization of Controlled Cooling of Forgings from Finishing Temperature with the Use of Light and Electron Microscopy [J]. Manufacturing Technology.
  8. WEI Z D, DUN Z S Influences of friction condition and end shape of billet on convex at root of spline by rolling with round dies[J]. Manufacturing Technology.
  9. WANG, ZHUN. Research on the design of a millturn center [J]. Manufacturing Technology.
  10. WANG D, XIANPING W U, CHEN L, et al. (2005). Improvement of the Hydraulic Circuit of Two Parallel Speed Control Valves Controlling Two Work Speeds and Its PLC Control System[J]. China Metalforming Equipment & Manufacturing Technology, 2005.
  11. SALLOOM M Y, SAMAD Z. (2011). Finite element modeling and simulation of proposed design magneto-rheological valve[J]. International Journal of Advanced Manufacturing Technology, 2011, 54(5-8):421-429. Go to original source...
  12. LIANG L, WEI D, WANG X, et al. (2016). Effects of hydraulic pressure on wrinkling and earing in micro hydro deep drawing of SUS304 circular cups[J]. International Journal of Advanced Manufacturing Technology, 2016:1-9.
  13. MORRIS G M, GOODSELL D S, HALLIDAY R S, et al. (2015). Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function[J]. Journal of Computational Chemistry, 2015, 19(14):1639-1662. Go to original source...
  14. BEYER H G. (1999). The simple genetic algorithm:foundations and theory[M]. 1999.
  15. TANG L, YUAN S, TANG Y, et al. (2019). Optimization of impulse water turbine based on GA-BP neural network arithmetic[J]. Journal of Mechanical Science and Technology, 2019, 33(1):241-253. Go to original source...

This is an open access article distributed under the terms of the Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.