A Cognitive Analysis and Life Prediction Through AI Algorithm of Control Arm Using Manufacturing and Vehicle Driving Data - Kim, Byeong Sam; Choi, Jinuk; Le, Sang Yeoul (Transactions on Engineering and Computing Sciences, 2025)
This study aims to enhance vehicle safety by predicting the life perdition of control arms, critical suspension components. Traditional inspection methods have limitations in accurately predicting failures, leading to unexpected accidents occurring both before and after the vehicle’s expected lifespan. The increasing complexity of control arm manufacturing, coupled with the growing volume of vehicle driving data and heightened competition, necessitates a more sophisticated approach to quality and safety. This system implements autonomy and intelligence of the production system by utilizing intelligent production system, big data, and artificial intelligence technologies, and supports optimal decision-making in real time. Data collection: There collected various sensor data from the production site, system data, MES system data, etc. In data refinement, data analysis and algorithm extraction of the Control Arm are performed, and the collected data is refined and preprocessed to be processed into a form suitable for analysis. Database construction: We build a relational database or NoSQL database to systematically manage data. This study represents a crucial step towards a more proactive and data-driven approach to vehicle safety and manufacturing. By integrating AI and big data technologies, the automotive industry can move towards a future characterized by minimized accidents and optimized production processes. Finally, we derived the results of predicting and optimizing the remaining useful life prediction of the remaining product.