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Due to several indeterminate factors, the assessment of the durability performance of a vehicle body is traditionally accomplished using test methods. An analytical fatigue life prediction method (four-step durability process) that relies mainly on numerical techniques is described in this paper. The four steps comprising this process include the identification of high stress regions, recognizing the critical load types, determining the critical road events and calculation of fatigue life. In addition to utilizing a general purpose finite element analysis software for the application of the Inertia Relief technique and a previously developed fatigue analysis program, two customized programs have been developed to streamline the process into an integrated, user-friendly tool. The process is demonstrated using a full body, finite element model.

Fatigue analysis using finite element models of a full vehicle body structure subjected to proving ground durability loads is a very complex task. The current paper presents an analytical procedure for fatigue life predictions of full body structures based on a time-domain approach. The paper addresses those situations where this kind of analysis is necessary. It also discusses the major factors (e.g., stress equivalencing procedure, cycle counting method, event lumping and load interactions) which affect fatigue life predictions in the procedure. A comparison study is conducted which explores the combination of these factors favorable for realistic fatigue life prediction. The concepts are demonstrated using a body system model of production size.

High mileage NVH performance is one of the major concerns in vehicle design for long term customer satisfaction. The current paper is concerned with performance analysis of high mileage vehicles which cover four automobile manufacturers and five vehicle families of the same weight class based on subjective evaluation data. The analysis includes the assessment of five vehicle families from the following aspects: overall and NVH performances, performance by individual attribute, degradation history of each vehicle family, performance variation within each vehicle family. Since the data are statistical in nature, statistical methods are employed, numerically and graphically, in the analysis. The performance categories which exhibit most degradation are identified. The analysis method presented in this paper is applicable to any high mileage vehicle fleet subjective data. The knowledge derived in the study can be used as a guideline in designing vehicles for high mileage NVH robustness.