TY - JOUR
AU - Xu, Li
AB - For the multi-objective optimization problem of the suspension lower pendulum arm structure, it is necessary to comprehensively consider the performance of stiffness, strength, and inherent frequency under different working conditions. This paper adopts the topology optimization method for optimal design, normalizes the sub-objectives based on the compromise planning method, constructs the comprehensive objective function, and determines the weight coefficients of the sub-objectives in the comprehensive objective function using grey relational analysis. Single-objective topology optimization is used to reduce the flexibility values under six working conditions, and the sub-sequence formed by single-objective topology optimization is subjected to grey relational analysis with the optimal sequence to obtain the weight coefficients of each sub-objective. The average frequency method is used to alleviate the oscillating phenomenon during the single-objective optimization of frequency. A comprehensive mathematical model is established for multi-objective topology optimization to analyze the effects of different static and dynamic weight coefficient assignments on the optimization results, and the optimal weight coefficient assignments are identified. Based on the topology optimization results, the lower pendulum arm is redesigned to be lightweight, and the static and dynamic performance analysis is carried out. The analysis results indicate that the performance of the optimized lower pendulum arm meets the design requirements, and the weight is reduced by 12% compared with the pre-optimization one, which has a significant effect on lightweight.
TI - Multi-objective topology optimization of the lower pendulum arm of a pick-up truck suspension
JF - "Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering"
DO - 10.1177/09544070251316684
DA - 2025-01-01
UR - https://www.deepdyve.com/lp/sage/multi-objective-topology-optimization-of-the-lower-pendulum-arm-of-a-AWZQV0esnG
VL - OnlineFirst
IS - 
DP - DeepDyve
ER -