Materials

Crankshafts are among the most important parts of modern internal combustion engines. Owing to the power transmission demand, sufficiently high strength is usually necessary for the application of the component. In this paper, a new crankshaft bending experimental method was proposed to shorten the corresponding test. A modified particle filtering algorithm approach was proposed for predicting the remaining fatigue life of a crankshaft during bending fatigue experiments. The predicted fatigue life was used to replace the actual experimental results for further analysis if the accuracy requirements were fulfilled; in this way, the experimental duration was obviously shortened. The main conclusion drawn from the research is that, compared with the traditional particle filtering algorithm approach, the modified particle algorithm approach proposed in this paper can more accurately predict the remaining fatigue life of a crankshaft using less experimental data, which makes it possible to circumvent actual bending fatigue experiments of crankshafts in providing theoretical guidance for the design process.

The pass reduction in hot rolling significantly influences the properties of 7075 alloy sheets, yet its quantitative effect requires systematic investigation. Multi-pass hot rolling experiments with 11% and 16% pass reductions were conducted on forged 7075 alloy. The microstructure, texture evolution, and mechanical properties were analyzed using SEM, EBSD, and mechanical testing. As the total thickness reduction increased, a clear correlation was observed with the enhanced mechanical properties of the hot-rolled 7075 alloy, demonstrated by the concurrent rise in both ultimate tensile strength (UTS) and yield strength (YS). When the total reduction exceeded 60%, the strengthening effect was most pronounced, with UTS and YS reaching 367.09 MPa and 332.82 MPa, respectively. The average grain sizes of 31.49 μm and 27.56 μm were achieved at the 12th pass (11% reduction per pass) and the 8th pass (16% reduction per pass), respectively. Under the condition of 11% reduction per pass, the texture intensity exhibited a non-monotonic trend with increasing passes. T6, T7, and RRA heat treatments were applied to the final rolled plates, and the maximum mechanical properties obtained in the hot-rolled 7075 plate following T6 heat treatment were UTS of 607.5 MPa, YS of 580.9 MPa, and elongation of 13.6%.

Selective Laser Melting (SLM), as a common metal additive manufacturing (AM) technology, achieves high-precision complex part formation by layer-by-layer melting of metal powder using a laser. However, the dynamic behavior of the melt pool during the SLM process is influenced by the heat source model, which is crucial for suppressing porosity defects and optimizing process parameters, directly determining the reliability of numerical simulations. To address the issue of traditional surface heat source models overestimating the melt pool width and volume heat source models underestimating the melt pool depth, this study constructs a three-dimensional transient heat conduction finite element model based on ANSYS Parametric Design Language (APDL) to simulate the evolution of the temperature field and melt pool geometry under different laser parameters. First, the temperature fields and melt pool morphology and dimensions of four heat source models—Gaussian surface heat source, volumetric heat source models (rotating Gaussian volumetric heat source, double ellipsoid heat source), and a combined heat source model—were investigated. Subsequently, a dynamic heat source model was proposed, combining a Gaussian surface heat source with a rotating volumetric heat source. By dynamically allocating the laser energy absorption ratio between the powder surface layer and the substrate depth, the influence of this heat source model on melt pool size was explored and compared with other heat source models. The results show that under the dynamic heat source, the melt pool width and depth are 128.6 μm and 63.13 μm, respectively. The melt pool width is significantly larger compared to other heat source models, and the melt pool depth is about 17% greater than that of the combined heat source model. At the same time, the predicted melt pool width and depth under this heat source model have relative errors of 1.0% and 5.5% compared to the experimental measurements, indicating that this heat source model has high accuracy in predicting the melt pool’s lateral dimensions and can effectively reflect the actual melt pool morphology during processing.