Thermal Fatigue of Automotive Components

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experimental and numerical development of the total strain – strain range partitioning method (TS-SRP) and the Sehitoglu method for TMF life predictions. The second project concentrates on the Chaboche method and four others for TMF life predictions. Some experiences with the testing procedures are discussed, together with issues of both material parameter determination and of numerical implementation of the three methods for general TMF cycles. Connections to finite element based analyses (FEA) are also explained, and difficulties with different approaches are discussed.
oxidation. The effects of different mechanisms can be either implicitly modeled, e.g. oxidation damage is implicitly included through some material parameters in the total strain – strain range partitioning method (TSSRP), or they can be separately and explicitly modeled, like in the Sehitoglu method. The TMF damage models are either strain based, e.g. TS-SRP, the Sehitoglu method, etc., or stress-based, e.g. Chaboche, Gallernau, etc. There is no single TMF life prediction method that is accepted by everyone. Several general methods are in widespread use within the community. Three of them are briefly discussed here: TS-SRP method by Manson and Halford, the Sehitoglu method and the Chaboche method. Only the Chaboche method involves concepts of continuum damage mechanics, which implicitly carries related effects of nonlinear damage accumulation. TOTAL STRAIN-STRAIN RANGE PARTITIONING METHOD – This is one of the earliest general methods capable of handling TMF life predictions. It was developed by Manson, Halford and Hirschberg (NASA) in 1971 [2], and has gone through several revisions and numerous applications. It was originally formulated on an inelastic strain range versus life basis for isothermal conditions (SRP), which was limited to short life regime. The present version (TS-SRP) is formulated on both elastic and inelastic strain ranges versus life basis [3-5], thus extending the method’s predictive capabilities to long life regime. The method has proven to be reliable, capturing all of the first order effects on TMF damage. Its application on general variable amplitude TMF loading is somewhat unclear, though, and requires experienced user. The method is based on the observation that the strain range of any TMF hysteresis loop can be partitioned into its basic components, depending on whether creep occurs and if it is in tension or compression. Total of four PP basic strain range components are identified: ∆ε PC (plastic in tension, plastic in compression), ∆ε (plastic CP CC in tension, creep in compression), ∆ε , and ∆ε . The first superscript identifies tension part and the second identifies compression part of a cycle. There are two different aspects of material behavior that are considered in the TS-SRP method: failure behavior and flow behavior. Failure behavior is characterized in the short life regime (e.g. 1,000 to 10,000 cycles), where testing times are reasonable. Failure data are very much time and temperature insensitive. Flow behavior, on the other hand, is characterized in the long life regime (e.g. over 100,000 cycles) by cyclic tests only until hysteresis loops stabilize, thus keeping the testing times to minimum (TMF testing in long life regimes with different creep hold times is typically prohibitively expensive). Flow data are extremely time and temperature sensitive, so different flow tests have to be conducted for a variety of component service conditions.
2001-01-0829
Thermal Fatigue of Automotive Components
Vladimir Ogarevic
nCode International
Bruce Whittle
Ford Motor Company
Xiaobin Lin
nCode International
Robin Anderson
nCode International
Copyright © 2001 Society of Automotive Engineers, Inc.
ABSTRACT
Modern approaches for thermal fatigue damage assessment in automotive components are discussed. Three prominent methods are reviewed, and issues with related material testing, numerical implementations and applications to general thermal cycles are presented. In summary, the chosen methods can produce good thermal fatigue life predictions. Common difficulties include first, prolonged experimental programs to determine the required material parameters, and second, significant computational times involved in analysis of realistic models and loading histories.