Uncertainty-based multi-disciplinary multi-objective design optimization of unmanned mining electric shovel

Electric shovel (ES) is a large mining equipment crucial for energy security. The traditional design of the structure and control system of ES is carried out in stages, and the influence of the structural uncertainty for the system is not considered, which makes it difficult to obtain the optimal parameters of the system. Facing the demand of intelligent development, ES designed using traditional deterministic methods is difficult to meet the working demand of unmanned mining electric shovel (UMES). To address these challenges, this paper proposes an uncertainty-based multidisciplinary multi-objective optimization (UMMO) framework for UMES. Within this framework, the mechanical structure of the front-end mechanism was analyzed, excavation trajectories were planned based on a polynomial point-to-point motion strategy, and models for the excavation resistance of the dipper and the dynamical model of the front-end working device were constructed. Then, optimization objective functions were constructed with excavation energy consumption, excavation efficiency, and full dipper rate as targets. By analyzing the working characteristics of UMES, essential constraints were introduced for the mechanical system, control system and hardware. The UMMO optimization model was established to enhance the reliability of the UMES production process. Finally, the mechanical structure dimensions and control system parameters are optimized to generate the optimal physical structure and excavation trajectory considering uncertainties. The numerical results show that compared with the deterministic optimization results, the optimized structure of the proposed UMMO strategy is more compact and the mechanical structure is more reliable in the production process.