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This is a Validation Study study.

Data-driven surrogate model establishment and performance optimization for cab and suspension systems

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Surrogate model significantly enhances performance index by 19.4% in multi-body dynamics, indicating effective vibration optimization strategies.

Performance index improved by 19.4% through multi-objective optimization, enhancing overall system efficiency.
Neural network model offers superior fitting accuracy and establishes a high-fidelity surrogate model with 96.4% fidelity.
Research employed multi-body dynamics models to assess vibration performance and identify critical variables influencing outcomes.
Optimization strategies revealed that enhancing spring and damping parameters effectively improves vehicle posture performance.

Quan et al. (2025) studied this question.

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Surrogate model significantly enhances performance index by 19.4% in multi-body dynamics, indicating effective vibration optimization strategies.