Optimization of Vehicle Powertrain Mounting System Based on Generalized Inverse Cascade Method under Uncertainty

This paper presents a summary of the optimization design process for multi-objective, two-level engineering problem, utilizing generalized inverse cascade method under uncertainty. The primary objective is to enhance vibration isolation performance mounting system, considering influence uncertain factors on its stiffness. focus determining value range variables at bottom layer, ensuring that goal met with specified confidence level. To illustrate application this methodology, powertrain mount used as case study. A data-driven approach adopted, establishing quantitative mapping relationship between stiffness, force transmission rate, modal decoupling and other indicators. achieved through development CRBM-DBN approximate model, which combines Conditional Restricted Boltzmann Machines (CRBMs) Deep Belief Network (DBN). Additionally, an intelligent algorithm interval search technology are employed determine optimal Simulation experimental verification conducted using selected parameter combinations. results demonstrate notable improvements in performance, vehicle NVH when compared original state. These findings provide valuable insights similar well problems, serving useful references future research applications.