Accelerating Product Development Life Cycle through Digitalization of Fatigue Life for Structural Components

In today’s competitive market, accelerating product development without compromising durability is critical. Durability is a key benchmark for assessing the quality and reliability of automotive products. To meet rising customer expectations and regulatory demands, the industry must develop vehicles that offer high performance and reduced weight while ensuring safety. A well-engineered vehicle must retain structural integrity and functionality under normal operating conditions and withstand extreme load events without critical failure. Achieving this requires effective Road Load Data Acquisition (RLDA), integrated with robust design practices and efficient validation processes. However, physical RLDA is time-consuming and costly, as it depends on prototype vehicles that are often available only in the later development stages. Failures identified during these late-stage tests can delay the product launch significantly. To address these challenges, this paper introduces a digital methodology for fatigue life estimation using Digital Road Load Data Acquisition (Digital RLDA). By leveraging virtual sensors and simulation-based techniques, Digital RLDA enables the reconstruction of real-world loading conditions early in the design phase. This allows for proactive durability assessments without extensive reliance on physical prototypes. Fatigue life estimation is performed using virtual Rain flow Linear Damage Accumulation (vRLDA), which extends traditional Rain flow counting and the Palmgren-Miner rule by incorporating advanced load reconstruction and cycle counting in a virtual environment. This approach simulates realistic service conditions, extracts load spectra, and predicts fatigue damage and remaining useful life with high accuracy. The proposed digital framework is validated against experimental results and shows strong correlation with actual fatigue behavior. It provides a reliable and efficient tool for early-stage fatigue assessment, supporting faster design iterations, reduced development costs, and improved maintenance planning.