Multidisciplinary co-design optimization of structural and control parameters for bucket wheel reclaimer

Abstract

Bucket wheel reclaimer (BWR) is an extremely complex engineering machine that involves multiple disciplines, such as structure, dynamics, and electromechanics. The conventional design strategy, namely, sequential strategy, is structural design followed by control optimization. However, the global optimal solution is difficult to achieve because of the discoordination of structural and control parameters. The co-design strategy is explored to address the aforementioned problem by combining the structural and control system design based on simultaneous dynamic optimization approach. The radial basis function model is applied for the planning of the rotation speed considering the relationships of subsystems to minimize the energy consumption per volume. Co-design strategy is implemented to resolve the optimization problem, and numerical results are compared with those of sequential strategy. The dynamic response of the BWR is also analyzed with different optimization strategies to evaluate the advantages of the strategies. Results indicate that co-design strategy not only can reduce the energy consumption of the BWR but also can achieve a smaller vibration amplitude than the sequential strategy.

Keywords

bucket wheel reclaimer / co-design / energy-minimum optimization / sequential strategy

References

[1] Bošnjak S M, Arsić M A, Zrnić N Đ, Bucket wheel excavator: Integrity assessment of the bucket wheel boom tie-rod welded joint. Engineering Failure Analysis, 2011, 18(1): 212–222

[2] Chatterjee A, Das D. A review of bucket wheel reclaimer failure through mechanical test and metallographic analysis. Journal of Failure Analysis and Prevention, 2014, 14(6): 715–721

[3] Bošnjak S, Pantelić M, Zrnić N, Failure analysis and reconstruction design of the slewing platform mantle of the bucket wheel excavator O&K SchRs 630. Engineering Failure Analysis, 2011, 18(2): 658–669

[4] Lu T F. Preparation for turning a bucket wheel reclaimer into a robotic arm. In: Proceedings of the International Conference on Robotics and Biomimetics. Bangkok: IEEE, 2008, 1710–1715

[5] Lu T F. Bucket wheel reclaimer modeling as a robotic arm. In: Proceedings of the International Conference on Robotics and Biomimetics. Guilin: IEEE, 2009, 263–268

[6] Zhao S, Lu T F, Koch B, A simulation study of sensor data fusion using UKF for bucket wheel reclaimer localization. In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE). Seoul: IEEE, 2012, 1192–1197

[7] Yang L, Qi J, Li S, Collaborative optimization for train scheduling and train stop planning on high-speed railways. Omega, 2016, 64: 57–76

[8] Yuan Y L, Song X G, Sun W, Multidisciplinary design optimization of the belt drive system considering both structure and vibration characteristics based on improved genetic algorithm. AIP Advances, 2018, 8(5): 055115

[9] Li W, Wang P, Li D, Multiphysical field collaborative optimization of premium induction motor based on GA. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1704–1710

[10] Zhao T, Liu D, Chen Q. A collaborative optimization method for heat transfer systems based on the heat current method and entransy dissipation extremum principle. Applied Thermal Engineering, 2019, 146: 635–647

[11] Sun W, Wang X B, Wang L T, Multidisciplinary design optimization of tunnel boring machine considering both structure and control parameters under complex geological conditions. Structural and Multidisciplinary Optimization, 2016, 54(4): 1073–1092

[12] Sun W, Wang X B, Shi M L, Multidisciplinary design optimization of hard rock tunnel boring machine using collaborative optimization. Advances in Mechanical Engineering, 2018, 10(1): 1687814018754726

[13] Huang Z L, Zhou Y S, Jiang C, Reliability-based multidisciplinary design optimization using incremental shifting vector strategy and its application in electronic product design. Acta Mechanica Sinica, 2018, 34(2): 285–302

[14] Bidoki M, Mortazavi M, Sabzehparvar M. A new approach in system and tactic design optimization of an autonomous underwater vehicle by using multidisciplinary design optimization. Ocean Engineering, 2018, 147: 517–530

[15] Garg D, Patterson M A, Francolin C, Direct trajectory optimization and costate estimation of finite-horizon and infinite-horizon optimal control problems using a Radau pseudospectral method. Computational Optimization and Applications, 2011, 49(2): 335–358

[16] Janson L, Schmerling E, Pavone M. Monte Carlo motion planning for robot trajectory optimization under uncertainty. In: Bicchi A, Burgard W, eds. Robotics Research. Cham: Springer, 2018, 343–361

[17] Wang X B, Sun W, Li E Y, Energy-minimum optimization of the intelligent excavating process for large cable shovel through trajectory planning. Structural and Multidisciplinary Optimization, 2018, 58(5): 2219–2237

[18] Tian L, Collins C. An effective robot trajectory planning method using a genetic algorithm. Mechatronics, 2004, 14(5): 455–470

[19] Franke R. Scattered data interpolation: Tests of some methods. Mathematics of Computation, 1982, 38(157): 181–200

[20] Rippa S. An algorithm for selecting a good value for the parameter c in radial basis function interpolation. Advances in Computational Mathematics, 1999, 11(2–3): 193–210

[21] Wu Z, Schaback R. Local error estimates for radial basis function interpolation of scattered data. IMA Journal of Numerical Analysis, 1993, 13(1): 13–27

[22] He E J. Cantilever wheel arm gyral machine for heaping and taking the material 1/cosϕ speed control system. The World of Inverters, 2006, 10(5): 106–107 (in Chinese)

[23] Chudnovskii V Y. Vertical oscillations of the working unit of a bucket-wheel excavator in a pit face and their suppression. Journal of Machinery Manufacture and Reliability, 2008, 37(3): 221–227

[24] Wolpert D H, Macready W G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67–82

[25] Yuan Y L, Lv L Y, Wang X B, Optimization of a frame structure using the Coulomb force search strategy-based dragonfly algorithm. Engineering Optimization, 2020, 52(6): 915–931

Acknowledgements

The research was supported by the National Key R&D Program of China (Grant Nos. 2018YFB1700704 and 2018YFB1702502).

RIGHTS & PERMISSIONS

2020 Higher Education Press