Free Access
Issue
Mechanics & Industry
Volume 21, Number 4, 2020
Article Number 402
Number of page(s) 14
DOI https://doi.org/10.1051/meca/2019086
Published online 06 May 2020
  1. C. Wang, Y. Han, S. Wang, Pressure control of rotary accumulator, Int. Conf. Fluid Power Mechatron. 14, 1395–1398 (2015) [Google Scholar]
  2. V.E. Geller, Characteristic features of necking during drawing and ultrahigh-speed spinning of polyethylene phthalate yarns, Review, Fibre Chem. 48, 1–11 (2016) [CrossRef] [Google Scholar]
  3. Z.X. Li, X.D. Shu, Numerical and experimental analysis on multi-pass conventional spinning of the cylindrical part with GH3030, Int. J. Adv. Manuf. Technol. 103, 2893–2901 (2019) [Google Scholar]
  4. H. Wu, W.C. Xu, D.B. Shan, Mechanism of increasing spinnability by multi-pass spinning forming − Analysis of damage evolution using a modified GTN model, Int. J. Mech. Sci. 159, 1–19 (2019) [CrossRef] [Google Scholar]
  5. B. Sun, J. Guo, Y. Lei, Simulation and verification of a non-equilibrium thermodynamic model for a steam catapult's steam accumulator, Int. J. Heat Mass Transf. 85, 88–97 (2015) [Google Scholar]
  6. R. Tomisawa, T. Ikaga, K.H. Kim, et al., Effect of melt spinning conditions on the fiber structure development of polyethylene terephthalate, Polymer 116, 367–377 (2016) [Google Scholar]
  7. Q.H. Su, F. Peng, J. Xing, Experimental research on advanced accumulator, Yuanzineng Kexue Jishu/At. Energy Sci. Technol. 51, 636–640 (2017) [Google Scholar]
  8. W. Latas, J. Stojek, A new type of hydrokinetic accumulator and its simulation in hydraulic lift with energy recovery system, Energy 153, 836–848 (2018) [CrossRef] [Google Scholar]
  9. H. Kadakia, A. Baker, M. Paulsen, A mechanistic accumulator model for RETRAN-3D, Nucl. Technol. 202, 71–80 (2018) [Google Scholar]
  10. B. Ryszard, B. Zbigniew, H.S. Anna, Methodology and a continuous time mathematical model for selecting the optimum capacity of a heat accumulator integrated with a CHP plant, Energies 11, 1240 (2018) [Google Scholar]
  11. I. Victor, S.A. Casper, Hydraulic pitch control system for wind turbines: Advanced modeling and verification of an hydraulic accumulator, Simul. Model. Pract. Theory. 79, 1–22 (2017) [Google Scholar]
  12. S.M. Ostroumov, Choice and optimization of parameters of a transpiration cooling accumulator, Inzhenerno-Fizicheskii Zhurnal 60, 918–922 (1991) [Google Scholar]
  13. J. Lee, U.Y. Lee, Design optimization of an accumulator for reducing rotary compressor noise, Proc. Inst. Mech. Eng. 226, 285–296 (2012) [CrossRef] [Google Scholar]
  14. J.Z. Hui, Y.K. Yang, H.Y. Zhang, Braking energy recovery system and control optimization of excavator based on accumulator, Chin. J. Highway Transp. 29, 143–151 (2016) [Google Scholar]
  15. M. Yu, B.Q. Shi, Optimization design and robust analysis of accumulator in hydraulic brake system, Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agri. Eng. 27, 132–136 (2011) [Google Scholar]
  16. Q.D. Zhu, P. Lu, Z.B. Yang, Multi-parameter optimization for the wet steam accumulator of a steam-powered catapult, Energies 12, 234 (2019) [Google Scholar]
  17. A. Hashemi, M.H. Gollo, Application of a new integrated optimization approach in sheet hydroforming process, Mech. Ind. 19, 303 (2018) [CrossRef] [Google Scholar]
  18. U.Y. Lee, B.J. Kim, J.B. Lee, Design optimization of an accumulator for noise reduction of rotary compressor, Trans. Korean Soc. Mech. Eng. 35, 759–766 (2011) [CrossRef] [Google Scholar]
  19. Q.J. Chen, Z.X. Xu, X.H. Yue, Characteristic modeling and parameter optimization of the accumulator in hydraulic power take-off system for wave power generation, Yingyong Jichu yu Gongcheng Kexue Xuebao/J. Basic Sci. Eng. 27, 226–237 (2019) [Google Scholar]
  20. Y.C. Lin, S.S. Qian, X.M. Chen, Staggered spinning of thin-walled Hastelloy C-276 cylindrical parts: Numerical simulation and experimental investigation, Thin-Walled Struct. 140, 466–476 (2019) [CrossRef] [Google Scholar]
  21. M. Li, Y.Y. Xu, H. Li, A novel spinning process for simultaneously producing two cone parts with big angle, J. Chin. Inst. Eng. 41, 547–556 (2018) [CrossRef] [Google Scholar]
  22. S. Mohammad, J. Iraj, K.N. Mehdi, Experimental study and FEM analysis of forward hot dieless spinning, Mech. Ind. 19, 404 (2018) [CrossRef] [Google Scholar]
  23. T. Yoichi, K. Shigefumi, N. Takuo, Effects of neck length on occurrence of cracking in tube spinning, Proc. Manuf. 15, 1200–1206 (2018) [Google Scholar]
  24. W. Luo, F. Chan, B. Vu, et al. Study on compound spinning technology of large thin-walled parts with ring inner ribs and curvilinear generatrix, Int. J. Adv. Manuf. Technol. 98, 1199–1216 (2018) [Google Scholar]
  25. X. Zhiyong, R. Yuejuan, L. Wenbo, Effect of feed speed on aluminum alloy pipe neck-spinning process and deformation analysis via simulation, MATEC Web Conf. 67, 05011–05016 (2016) [CrossRef] [Google Scholar]
  26. X. Zhanga, L. Zhaoa, T. Wena, An optimized neck-spinning method for improving the inner surface quality of titanium domes, Procedia Eng. 207, 1731–1736 (2017) [Google Scholar]
  27. K.R. Biplov, Y.P. Korkolis, A. Yoshio, Experiments and simulation of shape and thickness evolution in multi-pass tube spinning, J. Phys. Conf. Ser. 1063, 012087–012092 (2018) [Google Scholar]
  28. S.M. Ghoreishian, M. Norouzi, A. Fereydooni, Optimization of melt-spinning parameters of poly(ethylene terephthalate) partially oriented multi-filament yarn in an industrial scale: Central composite design approach, Fibers Polym. 18, 1280–1287 (2017) [CrossRef] [Google Scholar]
  29. J.J. Cummins, S. Thomas, C.J. Nash, Experimental evaluation of the efficiency of a pneumatic strain energy accumulator, Int. J. Fluid Power. 18, 167–180 (2017) [CrossRef] [Google Scholar]
  30. A. Kumar, J. Das, K. Dasgupta, et al., Effect of hydraulic accumulator on pressure surge of a hydrostatic transmission system, J. Inst. Eng. (India) Ser. C 99, 169–174 (2017) [CrossRef] [Google Scholar]
  31. J. Cai, K. Wang, P. Zhai, A modified Johnson-Cook constitutive formula to predict hot deformation behavior of Ti-6Al-4V alloy, J. Mater. Eng. Perf. 24, 32–44 (2015) [CrossRef] [Google Scholar]
  32. L.Z. Zhou, L.M. Yang, Comparative study on constitutive models to predict flow stress of Fe-Cr-Ni preform reinforced Al-Si-Cu-Ni-Mg composite, J. Wuhan Univ. Technol. Mater. Sci. Ed. 32, 666–676 (2017) [CrossRef] [Google Scholar]
  33. M. Alitavoli, A. Darvizeh, M. Moghaddam, Numerical modeling based on coupled Eulerian-Lagrangian approach and experimental investigation of water jet spot welding process, Thin-Walled Struct. 127, 617–628 (2018) [CrossRef] [Google Scholar]
  34. X.-q. Chang, L.-y. Zhang, Y.-b. Yang, J.-l. Ren, Constitutive Models for Compressive Deformation of AZ80 Magnesium Alloy under Multiple Loading Directions and Strain Rates, J. Iron Steel Res. Int. 23, 64–68 (2016) [CrossRef] [Google Scholar]
  35. D.-N. Zhang, Q.-Q. Shangguan, C.-J. Xie, F. Liu, A modified Johnson-Cook model of dynamic tensile behaviors for 7075-T6 aluminum alloy, J. Alloys Compd. 619, 186–194 (2015) [Google Scholar]
  36. M. Machorro-López José, A. Bellino, S. Marchesiello, Wavelets-based damage localization on beams under the influence of moving loads, Mechanics 14, 107–113 (2013) [Google Scholar]
  37. K. Erik, Meshing recommendations for the P-approach application in ABAQUS − A tool for pheno-numerical spring-in prediction, Compos. Struct. 203, 1–10 (2018) [Google Scholar]
  38. S.Q. Zhang, J.X. Yan, L. Cao, Crack Propagation Simulation of Hot Mill Grinding with Wood Based on ADAMS and ABAQUS, Linye Kexue/Scientia Silvae Sinicae. 54, 149–156 (2018) [Google Scholar]
  39. Z.R. Yang, X.L. Bai, Y.H. Xie, Finite element analysis on the collision between serial risers by using ABAQUS software, J. Vib. Shock 36, 196–200 (2017) [Google Scholar]
  40. X. Shi, P. Teixeira, J. Zhang, Kriging response surface reliability analysis of a ship-stiffened plate with initial imperfections, Struct. Infrastruct. Eng. 89, 1–16 (2014) [Google Scholar]
  41. X.L. Jia, J. Wang, Y.L. Zhang, True stress and shakedown analysis of pressure vessel under repeated internal pressure, Mech. Ind. 17, 410 (2016) [CrossRef] [Google Scholar]
  42. C.N.C. Bhatra, S.A. Saheb, Note on reduction of dimensionality for second order response surface design model, Commun. Stat. Theory Methods 46, 3520–3525 (2016) [Google Scholar]
  43. R.K. Kamaraj, J.G. Thankachi Raghuvaran, A.F. Panimayam, Performance and exhaust emission optimization of a dual fuel engine by response surface methodology, Energies 11, 3508 (2018) [Google Scholar]
  44. W.S. Liu, X.M. Yao, C.Q. Li, Optimization of configuration parameters of tail-sitter UAV based on response surface and genetic algorithm, Trans. Chin. Soc. Agri. Mach. 50, 88–95 (2019) [Google Scholar]
  45. A. Caglar, T. Sahan, M.S. Cogenli, A novel central composite design based response surface methodology optimization study for the synthesis of Pd/CNT direct formic acid fuel cell anode catalyst, Int. J. Hydrogen Energy 43, 11002–11011 (2018) [Google Scholar]
  46. C. Lu, L. Gao, X. Li, Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm, J. Clean. Prod. 137, 1516–1531 (2016) [Google Scholar]
  47. M.A. Sahali, I. Belaidi, R. Serra, New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm, Int. J. Adv. Manuf. Technol. 83, 1265–1279 (2016) [Google Scholar]
  48. J. Song, J. Li, Q.H. Yang, Multi-objective optimization and its application on irrigation scheduling based on AquaCrop and NSGA-II, J. Hydraulic Eng. 49, 1284–1295 (2018) [Google Scholar]
  49. J. Huang, Z.B. Chen, Q.M. Liu, Multi-objective optimization for laser closure process parameters in vitro skin tissue based on NSGA-II, Chin. J. Lasers 46, 0207001 (2019) [CrossRef] [Google Scholar]
  50. L.B. Huo, Z.Q. Cao, F. Zhang, Numerical and experimental study on TC4-DT titanium alloy structure after double cold expansion, J. Northwest. Polytech. Univ. 36, 701–708 (2018) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.