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The mechanical claw is an important functional part of intelligent sanitation vehicles. Its performance significantly influences the functional reliability and structural safety of intelligent sanitation vehicles. The load of the trash changes extensively during the work of the mechanical claw. Hence, a comprehensive consideration of structural uncertainty during designing is needed to meet performance requirements. Uncertainty optimization design should be applied to reduce the sensitivity of structural performance to uncertain factors and ensure the robust performance of the mechanical paw structure. In this study, a numerical model of the mechanical claw of novel intelligent sanitation vehicles is established first in SolidWorks, and a finite element model is built by Optistruct. Based on the analysis of uncertain load factors of the mechanical claw, a robust mathematical model of uncertain factors is established by the Gauss-Chebyshev and Smolyak algorithm.

The performance of the rear axle plays an important role in the performance of vehicle, and its fatigue durability is an integral part in the vehicle development. Taking a SUV model as the research subject, a new methodology of multi-channel spindle coupled road simulator and fatigue simulation analysis for rear axle assembly was introduced in the paper, aiming to address the fatigue design and its verification for the rear axle in the development phase. Firstly, road loads in the proving ground was collected by arranging proper sensors. Secondly, physical iteration was performed on the multichannel spindle coupled road simulator by taking six component forces at the wheel hub as the target signals. Then, after the time waveform replication of the loads the durability test was conducted. Finally, the validated simulation model was successfully implemented to improve the fatigue life of the axle.