This paper presents a summary of the optimization design process for a multi-objective, two-level engineering problem, utilizing the generalized inverse cascade method under uncertainty. The primary objective is to enhance the vibration isolation performance of a mounting system, considering the influence of uncertain factors on its stiffness. The focus is on determining the value range of the design variables at the bottom layer, ensuring that the design goal is met with a specified confidence level. To illustrate the application of this methodology, the optimization design of a powertrain mount is used as a case study. A data-driven approach is adopted, establishing a quantitative mapping relationship between mount stiffness, force transmission rate, modal decoupling rate, and other design indicators. This is achieved through the development of a CRBM-DBN approximate model, which combines Conditional Restricted Boltzmann Machines (CRBMs) and a Deep Belief Network (DBN). Additionally, an intelligent optimization algorithm and interval search technology are employed to determine the optimal design interval for the mount stiffness. Simulation and experimental verification are conducted using selected parameter combinations. The results demonstrate notable improvements in the vibration isolation performance, modal decoupling rate, and vehicle NVH performance when compared to the original state. These findings provide valuable insights for the interval optimization design of similar multi-objective, as well as two-level engineering problems, serving as useful references for future research and applications.
With the increasing awareness of the importance of environmental protection and the fierce competition in the construction machinery market, improving the vibration comfort of a whole construction machine has become a new focus of competition; therefore, optimizing the performance of cab mounts has become an urgent problem to be solved. At present, the problems of low modeling efficiency, serious technical difficulties, and long development cycles exist in the design and optimization of cab mounts. In this paper, a multi-target regression forests method is introduced into the design and optimization of the construction machinery installation system, which circumvents the traditional complex modeling process and establishes a mapping relationship between cab assembly parameters and the mounts’ stiffness, as well as introduces the system decoupling rate and vibration isolation rate as the boundary conditions. Furthermore, the MRFs method is compared and evaluated with MLRP and Multi-SVR prediction results. Finally, a complete, accurate, and efficient design method for the cab mount system optimization is developed, improving the decoupling rate and vibration isolation rate of the cab system. This design method can predict the stiffness of the mounts in multiple directions.
The seat of a construction machinery cab is used as the research object. For the current human-seat coupling system comfort research methods and optimization index deficiencies, the seat body pressure comfort and vibration comfort at the same time optimized. Based on the more specialized Toyota 50 percentile dummy model, a human-seat finite element simulation model is established, and the body pressure distribution and vibration response are simulated and calculated. The transverse and longitudinal pressure distributions of the backrest and seat cushion and the pressure map are used to verify the simulation model’s body pressure comfort evaluation indexes. At the same time, the vibration response test is used to verify the vibration comfort evaluation indexes of the simulation model. The test results show that the accuracy of each evaluation index of the established coupling model is greater than 85%, which can provide model support for the subsequent optimization work. In order to improve the comfort of the seat of construction machinery during operation, the hardness of the upper sponge and lower layer sponge is reduced and increased by 10% and 15%, respectively, on the original seat. The body pressure comfort evaluation indexes of the ischium peak pressure, ischium mean pressure, thigh peak pressure and thigh mean pressure are used to evaluate the improved seat. The proposed optimization scheme is to reduce the hardness of the upper sponge and lower layer sponge of the seat cushion by 10% to improve the seat body pressure comfort. Finally, the evaluation indexes of body pressure comfort and vibration comfort are verified by four subjects in an improved seat, and the cushion pressure of different subjects is reduced while the vibration isolation rate is increased, which shows the rationality of the proposed optimization scheme. In addition, the evaluation results of the improved seat are different for subjects of different body sizes, with the most significant improvement for the subject of greater height and weight. The modeling and comfort evaluation methods adopted in the paper can provide a reference for the design and development of the seat.
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