A through process model of the impact of in-service loading, residual stress, and microstructure on the final fatigue life of an A356 automotive wheel
Introduction
In transport applications, components manufactured from cast aluminium alloys offer an improved strength-to-weight ratio and better fuel efficiency relative to ferrous alloys. The cyclic nature of in-service loading in some applications makes fatigue performance a key design consideration. A predictive tool is required to assist in design from a fatigue perspective. The fatigue life of a component is intrinsically determined by alloy composition, microstructural features, and the presence and size of defects. Extrinsic factors such as residual stress and in-service loading also influence fatigue life. For cast aluminium alloy components, the manufacturing process, comprised of casting, heat treatment (including solution, quenching and artificial ageing steps) and machining, influences the intrinsic fatigue strength and the residual stress state. Therefore, the required predictive tool should link the influence of the manufacturing process with in-service loading to predict the final fatigue behaviour of the component, termed through process modelling [1]. Each of the critical manufacturing sub-processes is linked to an overall service model that incorporates the key intrinsic and extrinsic factors (as shown in Fig. 1).
The first step in this methodology, corresponding to the first stage of manufacturing, is the development of a casting model to predict microstructural features and defects, such as secondary dendrite arm spacing and porosity. The second step is to simulate the T6 heat treatment to predict the residual stress distribution developed during quenching. In the third step, the residual stress relief occurring upon removal of a layer of surface material during machining is predicted. The fourth step focuses on the in-service behaviour, predicting the stress state arising from the applied cyclic load and the residual stress. In the final step, the performance is predicted by linking the microstructural features and stress state in fatigue life calculations.
In previous studies, the authors have demonstrated the viability of the through process modelling methodology, validating separately the casting [2], residual stress [3], and fatigue [4], [5], [6] components. However, the application of this methodology to an aluminium alloy wheel including in-service loading has not been reported. A few authors have predicted the variation of stress in a wheel due to service loads during radial [7] or bending [8], [9], [10] fatigue tests. However, these studies did not incorporate the residual stress distribution resulting from manufacturing, which is necessary to calculate the in-service stress distribution.
In the current investigation, a validated in-service stress model of a bending fatigue test employed to assess A356 automotive wheels was linked with a previously reported through process model. This allowed the interaction of the key factors affecting fatigue behaviour to be quantified. The prediction of in-service cyclic strain variation under different bending loads is compared with experimental measurements of strain. The predicted fatigue lives of wheels tested at different loads are validated against bending fatigue test data.
Section snippets
Bending fatigue test
The bending fatigue test (SAE J328, Fig. 2) is used by some wheel manufacturers to assess the fatigue performance of wheels for loading conditions that are consistent with cornering (bending moment applied during continuous rotation). This test is part of standard operating practice to periodically assess product quality and qualify new designs. The inboard rim flange is centred and clamped securely to a rotating table. A rigid shaft is then attached to the hub section of the wheel and fastened
Model theory
The through process methodology includes the following modelling steps: (1) casting, (2) heat treatment, (3) machining, (4) in-service loading and (5) performance prediction. The microstructural features of maximum pore length (Lmax) and secondary dendrite arm spacing (λ2) are the key predictions of the casting step. The residual stress state is initially predicted in the heat treatment step and its relaxation is characterised in the machining step. The result from step 3 is combined with a
Results and discussion
Using the example of an A356 automotive wheel, the through process modelling methodology was applied to predict its performance during a bending fatigue test as a function of the microstructure and stress state induced during each processing stage. The key predictions and their validation are described for each processing step, followed by a discussion of the interaction of each step with the others.
Conclusions
A through process modelling methodology was developed to predict the final fatigue behaviour of an A356 automotive wheel during bending fatigue tests, resulting from the compounded influences of all four processing/service steps: (i) casting, (ii) T6 heat treatment, (iii) machining and (iv) the cyclic bending loads applied during service/testing.
The multiscale model predictions of microstructural features in the casting showed excellent agreement with measured values, including X-ray
Acknowledgements
P. Li gratefully acknowledges the financial support of the Stephen & Anna Hui Fellowship and the Overseas Research Student Award (ORS). Both P. Li and P.D. Lee acknowledge the Royal Academy of Engineering Global Research Awards. The authors also thank M.D. Lane and M. Ullattikulam of the University of British Columbia for their help during the experiments and the EPSRC (GR/T26344) for computational facilities support.
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