Peridynamic Open-Hole Tensile Strength Prediction of Fiber-Reinforced Composite Laminate Using Energy-Based Failure Criteria

The weight and modal performance of the vehicle wheels are two essential factors that affect the driving comfort of a vehicle. The main objective of this study is to present an efficient approach to reduce the weight and enhance the modal performance of the wheel by reducing the design time and computational cost. The alloy wheel rim is often used for lightweight wheel design. In this study, an approach is presented for the lightweight design of alloy wheel rims. An intelligent approach based on neural networks (NNs) is introduced to predict the optimum design parameters of the wheel rim during the wheel design phase and to improve the wheel optimization process. The Latin hypercube and Hammersley designs of the experimental methods were used to obtain a training dataset with finite element analysis. The NN and multiple linear regression (MLR) models were trained to predict the weight, first-mode frequency, and displacement values. A multi-objective genetic algorithm was employed to optimize the design decisions based on the predicted values. It was used to compute the optimum results with both the NN and MLR models for a better prediction accuracy of the wheel rim design parameters. The proposed approach allows designers to optimize design decisions and evaluate design modifications during the early stages of the wheel development phase. The surrogate-based optimization method plays an important role in the wheel rim optimization process, particularly when the optimization model is established based on computationally expensive finite element simulations, testing, and prototypes. The results show the effectiveness of the NN-combined genetic optimization approach in predicting the responses and optimizing the design decisions for the alloy wheel rim design by reducing engineering time and computational cost.

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Fatigue life optimization and lightweight design of wheel based on entropy weight grey relation analysis and modified NSGA-II

Zhang,

Lu,

et al. 2023

Advances in Mechanical Engineering

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In order to study the fatigue performance of wheel and enhance its lightweight design level, this article proposes the structure, design and optimization method of magnesium-aluminum alloy assembled wheel. Taking a [Formula: see text] type wheel as the research object, the optimal topology of wheel spoke is solved by constructing a topology optimization model for wheel bending and radial fatigue test conditions. A finite element model for bending and radial fatigue testing of assembled wheels was established, which simulates and analyzes the fatigue performance and its influencing factors of the wheel under two working conditions. Combined with contribution analysis method, the modified NSGA-II and entropy weight grey relation analysis (EGRA), the multi-objective optimization of assembled wheel was performed. The result demonstrated that the weight reduction of the assembled wheel after optimized design is 4.49%, while the bending fatigue life and radial fatigue safety factor are decreased by 9.95% and 25%, respectively. In order to better balance the performance of assembled wheel and achieve lightweight design, this article combines the joint topology optimization of assembled wheels with multi-objective optimization method for multiple working conditions and screens the optimal compromise solution by EGRA, which provides an approach for wheel lightweight design and multi-objective optimization.

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Peridynamic Open-Hole Tensile Strength Prediction of Fiber-Reinforced Composite Laminate Using Energy-Based Failure Criteria

Cited by 4 publications

References 33 publications

Peridynamics for out-of-plane damage analysis of composite laminates

Peridynamics for out-of-plane damage analysis of composite laminates

Predictions of the design decisions for vehicle alloy wheel rims using neural network

Fatigue life optimization and lightweight design of wheel based on entropy weight grey relation analysis and modified NSGA-II

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