Load-Deflection Evaluation of Bump Stoppers Using a Machine Learning Approach

The accurate prediction and evaluation of load-deflection behavior in bump stoppers are critical for optimizing driving performance and durability in the automotive industry. Traditional methods, such as extensive experimental testing and finite element analysis (FEA), are often time-consuming and costly. This paper introduces a machine learning-based approach to efficiently evaluate load-deflection curves for bump stoppers, thereby streamlining the design and testing process. By leveraging a comprehensive dataset that includes historical test results, material properties, and geometrical dimensions, various machine learning methods, including Gradient boosting, Random forest & XG Boost were trained to predict load-deflection behavior with high accuracy. This approach reduces the reliance on extensive physical testing and simulations, significantly enhancing the design optimization process, leading to faster development cycles and more precise performance predictions. A case study is presented to demonstrate the effectiveness of this proposed machine learning-based process, highlighting its potential to transform traditional engineering practices and its broader applicability in evaluating other automotive components. The key requirement for this approach is the availability of sufficient data for training purposes. This paper explores the feasibility of utilizing data-driven techniques to enhance the design process of bump stoppers.