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Collaborative optimization of NURBS curve cross-section in a telescopic boom

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Abstract

To improve the carrying capacity and reduce the weight of telescopic boom structure in a truck crane, a Collaborative optimization (CO) approach was applied to solve the problems of strength, stiffness and local stability in the telescopic boom structure. First, the complex optimization problem of the telescopic boom structure was decomposed into two-level optimizations: the system level and two subsystem levels for strength and local stability. Second, the underside curve of the boom’s cross-section was constructed by the Non-uniform rational B-Splines (NURBS) curve. 3D parametric solid model and the parametric finite element analysis model for the strength and the local stability were then established. Third, the mathematical models of the strength and local stability for the subsystem levels, and the system level were optimized, respectively. The adaptive relaxation factor algorithm and the penalty function approach were applied to improve the efficiency of CO. Next, the CO process which integrates the ANSYS package with ISIGHT platform was implemented. The optimal results show that the carrying capacity of the telescopic boom structure can be significantly improved and its weight efficiently is reduced. Finally, with the comparison of the stress values obtained from both the experimental test and the theoretical computation, highly coincident results could be obtained to verify the reliability of CO of a telescopic boom.

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References

  1. Z. Nowak and W. Pogorzelski, Algorithm for the minimum weight design of a telescopic jib, Computers and Structures, 8 (1) (1978) 61–65.

    Article  Google Scholar 

  2. D. Derlukiewicz and G. Praybylek, Chosen aspects of FEM strength analysis of telescopic jib mounted on mobile platform, Automation in Construction, 17 (2008) 278–283.

    Article  Google Scholar 

  3. M. Savković et al., Stress analysis in contact zone between the segments of telescopic booms of hydraulic truck cranes, Thin-Walled Structures, 85 (2014) 332–340.

    Article  Google Scholar 

  4. W. Rust and K. Schweizerhof, Finite element limit load analysis of thin-walled structure by ANSYS (implicit), LSDYNA (explicit) and in combination, Thin-Walled Structures, 41 (2003) 227–244.

    Article  Google Scholar 

  5. J. Yao et al., Buckling failure analysis of all-terrain crane telescopic boom section, Engineering Failure Analysis, 57 (2015) 105–117.

    Article  Google Scholar 

  6. M. Gašić et al., Optimization of a pentagonal cross section of the truck crane boom using Lagrange’s multipliers and differential evolution algorithm, Meccanica, 46 (2011) 845–853.

    Article  MathSciNet  MATH  Google Scholar 

  7. G. Xu, X. Hou and Q. Dong, Based on the particle swarm optimization of truck crane telescopic jib optimization and implementation, Frontiers of Manufacturing Science and Measuring Technology III, Applied Mechanics and Materials, 401-403 (2013) 548–553.

    Google Scholar 

  8. Z. Wang et al., Reliability-based design optimization for box-booms considering strength degradation and random total working time, J. of Mechanical Science and Technology, 26 (7) (2012) 2045–2049.

    Article  Google Scholar 

  9. P. Beo, D. Kozak and P. Konjati, Optimization of thinwalled constructions in CAE system ANSYS, Tehnicki Vjesnik, 21 (5) (2014) 1051–1055.

    Google Scholar 

  10. U. Hamme and J. Henkel, Neue konzepte im leichtbauinnovativer teleskopausleger eines mobilkrans, Stahlbau, 82 (4) (2013) 246–249.

    Article  Google Scholar 

  11. I. Kroo et al., Multidisciplinary optimization methods for aircraft preliminary design, AIAA-94-1325.

  12. F. Xiong et al., A moment-matching robust collaborative optimization method, J. of Mechanical Science and Technology, 28 (4) (2014) 1365–1372.

    Article  Google Scholar 

  13. B. Roth and I. Kroo, Enhanced collaborative optimization: Application to an analytic test problem and aircraft design, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA 2008-5841.

  14. I. Budianto and J. Olds, Design and Deployment of a satellite constellation using collaborative optimization, J. of Spacecraft and Rockets, 41 (6) (2004) 956–963.

    Article  Google Scholar 

  15. T. Dylan and C. Matthew, A goal-programming enhanced collaborative optimization approach to reducing lifecyle costs for naval vessels, Structural and Multidisciplinary Optimization, 53 (2016) 1261–1275.

    Article  MathSciNet  Google Scholar 

  16. V. Ganesan, J. Rodriguez and R. Johnston, Collaborative design optimization for light weight design, SAE 2014 World Congress and Exhibition, Detroit, MI, United States (2014) 2014-1-0392.

    Google Scholar 

  17. C. McAllister and T. Simpson, Multidisciplinary robust design optimization of an internal combustion engine, J. of Mechanical Design, 125 (1) (2003) 124–130.

    Article  Google Scholar 

  18. R. Braun et al., Implementation and performance issues in collaborative optimization, AIAA-96-4017.

  19. N. M. Alexandrov and R. M. Lewis, Analytical and computational aspects of collaborative optimization for multidisciplinary design, AIAA J., 40 (2) (2002) 301–309.

    Article  Google Scholar 

  20. K.-S. Jeon, J.-W. Lee and J.-H. Choi, Efficient system optimization techniques through subspace decomposition and response surface refinement, AIAA-2002-0321.

  21. P. Fang et al., Asymptotic relaxation based on collaborative optimization, J. of Shanghai Jiaotong University, 47 (12) (2013) 1896–1901, 1906 (in Chinese).

    Google Scholar 

  22. X. Li, W. Li and C. Liu, Geometric analysis of collaborative optimization, Structural and Multidisciplinary Optimization, 35 (4) (2008) 301–313.

    Article  Google Scholar 

  23. X. Li et al., An alternative formulation of collaborative optimization based on geometric analysis, ASME J. of Mechanical Design, 133 (5) (2011) 051005 (1-11).

    Article  Google Scholar 

  24. X. Li et al., Application of collaborative optimization to optimal control problems, AIAA J., 51 (3) (2013) 745–750.

    Article  Google Scholar 

  25. H. Li, M. Ma and W. Zhang, Improving collaborative optimization for MDO problems with multi-objective subsystems, Structural and Multidisciplinary Optimization, 49 (2014) 609–620.

    Article  Google Scholar 

  26. H. Li, M. Ma and Y. Jing, A new method based on LPP and NSGA-II for multiobjective robust collaborative optimization, J. of Mechanical Science and Technology, 25 (5) (2011) 1071–1079.

    Article  Google Scholar 

  27. H. Li, M. Ma and W. Zhang, Multi-objective collaborative optimization using linear physical programming with dynamic weight, J. of Mechanical Science and Technology, 30 (2) (2016) 763–770.

    Article  Google Scholar 

  28. M. Liu et al., Development and implementation of a NURBS interpolator with smooth feedrate scheduling for CNC machine tools, International J. of Machine Tools and Manufacture, 87 (2014) 1–15.

    Article  Google Scholar 

  29. R. Saravanan, S. Ramabalan and C. Balamurugan, Multiobjective trajectory planner for industrial robots with payload constraints, Robotica, 26 (6) (2008) 753–765.

    Article  Google Scholar 

  30. J. Rakowski and P. Wielentejczyk, Application of the difference equation method to the vibrations analysis of infinite Rayleigh beams by the isogeometric approach, Archives of Civil and Mechanical Engineering, 15 (4) (2015) 1108–1117.

    Article  Google Scholar 

  31. L. Piegl and W. Tiller, The NURBS Book, 2nd ed., New York: Springer (2010).

    MATH  Google Scholar 

  32. A. Ji et al., iFnite element analysis for local stability of thin-walled box section, 2008 Asia Simulation Conference-7th International Conference on System Simulation and Scientific Computing, IEEE Catalog Number: CFP0858DCDR: 542-546 (2008).

    Google Scholar 

  33. M. Ozakca, N. Taysi and F. Kolcu, Buckling analysis and shape optimization of elastic variable thickness circular and annular plates-I Finite element formulation, Engineering Structures, 25 (2003) 181–192.

    Article  Google Scholar 

  34. M. Ozakca, N. Taysi and F. Kolcu, Buckling analysis and shape optimization of elastic variable thickness circular and annular plates-II Shape optimization, Engineering Structures, 25 (2003) 193–199.

    Article  Google Scholar 

  35. Chinese National Standards Committee, GB/T 3811-2008 Design rules for cranes, Beijing: China Standard Press (2008) (in Chinese).

  36. A. Ji, X. Yin and M. Yuan, Hybrid collaborative optimization based on selection strategy of initial point and adaptive relaxation, J. of Mechanical Science and Technology, 29 (9) (2015) 3841–3854.

    Article  Google Scholar 

  37. W. H. Wang, C. H. Tseng and C. B. Tsay, On the optimization of spatial cam mechanisms considering mechanical errors, International J. of Modelling and Simulation, 19 (1) (1990) 94–100.

    Google Scholar 

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Authors and Affiliations

  1. College of Mechanical & Electrical Engineering, Hohai University, Changzhou, 213022, China

    Aimin Ji, Changsheng Chen, Liping Peng, Pin Lv & Xiaodi He

Authors
  1. Aimin Ji
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  2. Changsheng Chen
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  3. Liping Peng
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  4. Pin Lv
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  5. Xiaodi He
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Correspondence to Aimin Ji.

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Recommended by Associate Editor Gil Ho Yoon

Aimin Ji is a Professor of the College of Mechanical and Electrical Engineering, Hohai University, China. His research interests include digital design and manufacturing, CAD/CAE integration, and multidisciplinary design optimization.

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Ji, A., Chen, C., Peng, L. et al. Collaborative optimization of NURBS curve cross-section in a telescopic boom. J Mech Sci Technol 31, 3861–3873 (2017). https://doi.org/10.1007/s12206-017-0731-y

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  • DOI: https://doi.org/10.1007/s12206-017-0731-y

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