Browse Publications Technical Papers 2024-28-0027

Machine Learning Technique for Rubber Bush Simulation 2024-28-0027

Traditional rubber bush simulations typically utilize simple material models like hyperelastic or viscoelastic models. However, recent advancements introduce more sophisticated material models to capture the nonlinear and time-dependent behavior of rubber materials. These advanced models may incorporate nonlinear viscoelasticity, strain rate dependency, and damage mechanics. Emerging techniques go beyond traditional approaches by integrating microstructural details of rubber materials into the simulation. This includes modeling the arrangement and orientation of polymer chains, filler particles, and other constituents within the rubber matrix. Such microstructural modeling offers a more precise representation of material behavior under various loading conditions. Rubber bushings experience multiple physical phenomena simultaneously, such as mechanical loading, thermal effects, and fluid-structure interaction. New simulation techniques enable the coupling of different physics domains, allowing for a comprehensive analysis of bushing performance under realistic conditions. This paper explores the application of machine learning techniques to rubber bush simulation, aiming to enhance accuracy and efficiency. By training neural networks or other machine learning algorithms using experimental data, predictive models for rubber material behavior are developed. Overall, the new approach of rubber bush simulation facilitates more accurate and predictive analyses of bushing behavior, thereby improving the design and performance of automotive suspension systems and other applications. Key words: Rubber bush, Simulation, Machine Learning


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