A mine dump truck vibration comfort simulation optimization method and device

By constructing a multi-dimensional coupled simulation model, adaptive excitation reproduction, and subjective-objective integrated evaluation, combined with a digital twin closed-loop platform, the accuracy and efficiency issues in the vibration comfort simulation of mining dump trucks were solved, achieving coordinated suppression of vibration energy across the entire chain and efficient research and development.

CN122197486APending Publication Date: 2026-06-12XUZHOU XCMG MINING MACHINERY CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XUZHOU XCMG MINING MACHINERY CO LTD
Filing Date
2026-04-29
Publication Date
2026-06-12

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Abstract

The application discloses a mine dump truck vibration comfort simulation optimization method and device, comprising the following steps: S1, a cab-seat-human multi-dimensional coupling simulation model is constructed; S2, adaptive excitation replication from the actual vehicle to the six-degree-of-freedom vibration table is realized; S3, a vibration comfort subjective and objective fusion evaluation system is established; S4, taking the optimization of the comfort comprehensive index as the core target, the parameters of the seat suspension system and the cab suspension system are subjected to multi-objective collaborative optimization, and a vibration comfort optimization scheme is obtained; and S5, a digital twin closed loop platform is built. The application builds a "cab-seat-human" multi-dimensional coupling model containing refined human key parts, and gives it viscoelastic constitutive characteristics, realizes accurate characterization of the nonlinear mechanical response of the human body under strong vibration, makes the simulation model more truly reflect the transmission of vibration in the key parts of the human body, controls the model prediction error within 5%, and solves the problem of insufficient accuracy of the existing model.
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