Light weighting is significant in for automotive industry as it helps in less fuel consumption and to achieve better performance. Aluminium is a candidate material for light weighting. To design a component made of aluminium material, it is necessary to understand the fatigue performance of the material. In this paper, a study is carried out to understand the fatigue performance of aluminium 6xxx series alloys at an early stage of design without carrying out comprehensive fatigue testing. Coffin Manson Parameters are used to predict fatigue life. This research focusses on determining the gaps in existing models for aluminium alloys by carrying out comprehensive review of various models developed for 6xxx series which uses monotonic tensile data. Two models are developed and the predicted fatigue properties for this class of material are further compared with experimental fatigue, monotonic data and literature. Scatter band is used to show the correlation between the predicted and actual value of fatigue properties. In reversals to failure, 90 % of the data falls within five times scatter band, and in strain amplitudes, 90 % of data falls within two times scatter band. The models thus shows excellent in predicting strain amplitudes for a given number of fatigue life.


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    Titel :

    Method for Prediction of Coffin Manson Parameters from Monotonic Tensile Property for Aluminium 6XXX Series Alloy to Predict Fatigue Life


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Parasumanna, Ajeet Babu (Autor:in) / Saraf, M (Autor:in) / Suman, Shwetabh (Autor:in) / Ammu, Vishwanath (Autor:in)

    Kongress:

    Symposium on International Automotive Technology 2019 ; 2019



    Erscheinungsdatum :

    09.01.2019




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Method for Prediction of Coffin Manson Parameters from Monotonic Tensile Property for Aluminium 6XXX Series Alloy to Predict Fatigue Life

    Parasumanna, Ajeet Babu / Ammu, Vishwanath / Suman, Shwetabh et al. | British Library Conference Proceedings | 2019



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