Research on the Bending Fatigue Property of Quenched Crankshaft Based on the Multi-Physics Coupling Numerical Simulation Approaches and the KBM Model

Abstract

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.

Study on Temperature Field Uniformity of Dynamic Induction Heating for Camshaft of Marine Diesel Engine

“…where h is the surface convective heat transfer coefficient(W/m 2 • C), λ is the thermal conductivity, ∂T ∂n is the temperature gradient (the derivative of temperature in the n direction), n is the unit vector in the normal direction, T f is the ambient temperature ( • C), usually T f = 20 • C. h r is the radiative heat transfer coefficient, ε r is the radiation coefficient of the surface of the material, σ is the Boltzmann's constant, σ = 1.38•10 −23 (W/m 2 •K 4 ), and F a is the angular coefficient of thermal radiation. The convective heat transfer coefficient h between air and cam surface is 100 W/(m 2 • C), and the radiative heat transfer coefficient is 0.8 [27,28].…”

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

“…So we have based on the concept of mutation used in the genetic algorithm and optimized the PSO by introducing the inertia weight coefficient (a mutation operation), in order to expands the population search space, which otherwise continually contracts across iterations, thus enabling particles initialize the their position and velocity in model with a given probability to jump out of the highest quality position previously searched and carry out searches in a larger space, where the optimized PSO wound maintains population diversity and improve the probability in finding the global optimal solution, especially in solving complex functions [ 17 , 18 , 19 , 20 , 21 , 22 ]. Therefore, we optimize PSO to expand the population search space by introducing the inertia weight coefficient based on the concept of mutation in the genetic algorithm, which enables particles initialize their position and velocity in model with a given probability to jump out of the highest quality position previously searched and carry out searches in a larger space.…”

“…Recent research into modeling emergency response and resource allocation has made great use of bionic algorithms and particle swarm optimization [ 16 ]. However, machine learning algorithms usually converge locally at an early stage of modeling, making it difficult to obtain an optimal global solution to model equations [ 17 , 18 , 19 , 20 ].…”

The HCF property research of quenched crankshaft based on the stress field intensity method and the multi-physic field Coupling Numerical simulation approach

“…Based on the above research, a primary conclusion can be proposed that the main reason for the strengthening effect caused by the electromagnetic induction quenching approach is the compressive residual field generated at the stress concentration area [11][12] . In previous study, we conducted the critical plane theory and the selected multi-axial fatigue models in predicting the fatigue property of such crankshafts [13][14] . For these models, the range of application is usually related to the material property, the damage type and some other factors.…”