Prediction of fatigue life and estimation of its reliability on the parts of an air suspension system

• Published in: International journal of automotive technology Vol. 9; no. 6; pp. 741 - 747
• Main Authors: Jun, K. J., Park, T. W., Lee, S. H., Jung, S. P., Yoon, J. W.
• Format: Journal Article
• Language: English
• Published: Heidelberg The Korean Society of Automotive Engineers 01.12.2008; Springer Nature B.V; 한국자동차공학회
• Subjects: Automotive Engineering; Cost control; Engineering; Finite element analysis; Load; Materials fatigue; Mechanical engineering; Random variables; Roads & highways; Simulation; Studies; Vehicles; 자동차공학; Multibody dynamics; Fatigue life reliability; Modal stress recovery; Air suspension system; Pearson system; Flexible body dynamics
• ISSN: 1229-9138; 1976-3832
• DOI: 10.1007/s12239-008-0088-4

Air suspension systems have been implemented in various commercial vehicles, such as buses and special purpose trucks, because of the comfortable ride and easy height control. An evaluation of the durability of vehicle parts has been required for service life and safety starting in the early stages of design. The cyclic load applied to the vehicle can cause fatigue failure of parts, such as the suspension frame. This paper presents a method to predict the fatigue life of the suspension frame at the design stage of the air suspension system used in a heavy-duty vehicle. To estimate the fatigue life using the SN method, the Dynamic Stress Time History (DSTH) is necessary for the part of interest. DSTH can be obtained from the results of the flexible body dynamic analysis using the Belgian road simulation and the Modal Stress Recovery (MSR) method. Furthermore, the reliability of the predicted fatigue life can be evaluated by considering the variations in material properties. The probability and distribution of the expected life cycle can be obtained using experimental design with a minimum number of simulations. The advantage of using statistical methods to evaluate the life cycle is the ability to predict replacement time and the probability of failure of mass-produced parts. This paper proposes a rapid and simple method that can be effectively applied to the design of vehicle parts.