A Study on Two-Wheeler Main Stand Effort, Predicting Effort using Machine Learning Algorithms and Correlation with CAE

This paper explores motorcycle and scooter main stands, crucial for parking and maintenance. Main stand effort, influenced by vehicle layout, weight distribution, and stand design, is a key design target. The paper details methodologies for acquiring main stand effort data and recommends values for MBD and FEA correlation, serving as benchmarks. It also investigates using machine learning to predict main stand effort prior to the fabrication of physical components, reducing development costs and improving efficiency by identifying optimal designs early, thereby lessening the need for physical prototyping. By clarifying the main stand effort and providing guidance on data collection, analysis, and prediction, this paper aims to advance motorcycle and scooter design, enhancing user experience and vehicle functionality.