Development of a Predictive Model to Select Material for Motorcyclists' Impact Protector Design

Abstract

Motorcyclists are particularly vulnerable to injuries in the event of a crash due to the lack of physical protection compared to other vehicle occupants. Impact protectors are designed to mitigate the risk of injuries by absorbing and dissipating the impact energy. The selection of appropriate materials for impact protectors is crucial to ensure their effectiveness. This study aims to develop a predictive model using the XGBoost algorithm to assist in selecting the most suitable materials for motorcyclists' impact protector design. The model incorporates material properties and impact testing data to predict the performance of various materials under different impact scenarios. A comprehensive dataset was compiled, including material characteristics such as density, hardness, and tensile strength, as well as impact test results measuring parameters like peak force, energy absorption, and deformation. The XGBoost model was trained and validated using the collected data. The results demonstrate the model's ability to accurately predict the performance of materials in impact protectors, with a high degree of accuracy and reliability. Feature importance analysis was conducted to identify the most influential material properties in determining impact performance. The developed predictive model offers several advantages for the design and development of motorcyclists' impact protectors. It provides a data-driven approach to material selection, reducing the time and cost associated with traditional testing methods while ensuring the safety and effectiveness of the impact protectors. The model can be further refined and updated as new data becomes available, continuously improving its accuracy and reliability. This study highlights the potential of machine learning-based predictive modeling in optimizing material selection for impact protector design. The results emphasize the importance of integrating material properties and impact testing data to develop a comprehensive understanding of material performance under different impact scenarios. The predictive model presented in this study serves as a valuable tool for designers and engineers seeking to develop effective and safe impact protectors for motorcyclists and other applications.