Fatigue limit load is one of the most important and concerned factors in designing and manufacturing critical mechanical parts such as the crankshafts. Usually, this governing parameter is obtained by experiment, which is expensive, time-consuming and only feasible in analyzing the case of simple structure. Still, there's a big obstacle to clear to get the fatigue limit load of a sophisticated structure effectively and efficiently. This paper applied the stress field intensity theory to make quick component fatigue limit load predictions. First, the field diameter of a given crankshaft was determined based on its limit stress state and a stress distribution fitting approach. Then, this parameter was used to predict the high-cycle bending fatigue limit load of a new crankshaft composed of the same material. Finally, a corresponding experimental verification was conducted to evaluate the accuracy of the predictions. The results indicated that the original stress field intensity model may not be suitable due to the errors in the predictions, which can be attributed to the structural features. The new model proposed in this paper can provide higher accuracy in quick fatigue load prediction, making it superior to the traditional model in engineering application.

In modern engineering, electromagnetic induction quenching is usually adopted in improving the fatigue performance of steel engine parts such as crankshafts. In order to provide the theoretical basis for the design of the process, correct evaluation of the strengthening effect of this technique is necessary. In this paper, the research aim is the strengthening effect of this technique on a given type of steel crankshaft. First the magnetic-thermal coupling process was simulated by a 3D finite element model to obtain information on the temperature field during the heating and cooling stages. Then the residual stress field after cooling was simulated based on the same model. At last, the fatigue property of this crankshaft was predicted based on the combination of three parameters: the KBM (Kandil–Brown–Miller) multi-axial fatigue model, the residual stress field and the fatigue strength of the material. The experimental results showed that this method can achieve a much more reasonable prediction than the traditional strengthening factor, and thus can be applied in guiding the design of the quenching process.

For critical steel engine parts, such as crankshafts, the fatigue strength under the critical working condition is usually improved by the electromagnetic induction quenching technique. In a previous study, the strengthening effect of this approach was always evaluated by a constant, which may result in some errors with the change of the technological parameters. In this paper, a type of steel crankshaft is selected to study the strengthening effect of this approach; first a local sub model composed of the crankpin is built to simulate the magnetic–thermal coupling process, then, the residual stress field is determined by simulating the whole course of fabrication. Finally, the prediction of the fatigue limit load is proposed based on the residual stress and the strength parameters of the material. The experimental verification shows that, when compared to the general means of modification models, the modified McDiarmid multi-axial fatigue model is more suitable to be applied to analyze the fatigue property of this quenched crankshaft due to the markedly higher accuracy. Based on this study, a new fatigue-limit load-prediction approach of this kind of crankshaft can be proposed for engineering applications.

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In recent decades, the electromagnetic induction quenching approach has been widely applied in the surface treatment process of steel engine parts such as crankshafts. In this paper, the strengthening effect of this approach was selected to be the object of study. First, the multi-physics coupling phenomenon was established by a 3D finite element simulation approach. Then, the fatigue property of the crankshaft was predicted based on the combination of the residual stress field obtained in the previous step and a chosen multi-axial fatigue damage model. Finally, a corresponding experiment verification was carried out to check the accuracy of the prediction. The results showed that the method proposed by this paper can provide high enough accuracy in predicting the fatigue property of two types of commonly used steel crankshafts.