Dongdong You

To further refine rehabilitation protocols for transverse patellar fractures, this study conducted numerical simulations in Abaqus to investigate the interaction between a nickel–titanium shape-memory-alloy patellar concentrator and fractured patellar bone under variations in patellar loading, quadriceps muscle tension, knee-flexion angle, and fracture-site width. Lower knee-flexion angles primarily generated bending moments in the patellar concentrator, whereas greater flexion angles primarily generated tensile forces. Across the simulated knee-flexion range of 0–150°, the predicted quadriceps muscle force ranged from 240 to 670 N. Based on the simulated mechanical response, under the prescribed modeling assumptions, the 60–90° knee-flexion range showed a comparatively favorable balance between predicted fracture closure and implant loading. Comparison with previously published computational studies showed that the loading and angular conditions examined in this study were within the ranges reported in the literature, while extending the analysis to less frequently studied flexion conditions. However, because direct experimental validation, mesh-convergence testing, and comprehensive sensitivity analysis were not included, these values should be interpreted as model-based estimates requiring further experimental and clinical validation.

In squeeze casting, the injection parameters including fit clearance, speed, temperature, and their uncertainties significantly impact the forming quality. Robust optimization can improve the design reliability and reduce the influence of uncertainty while using a suitable metamodel is beneficial for prediction accuracy and efficiency. This paper proposes a robust optimization method based on a hybrid metamodel with Simplified Pareto and Updated Training Set (SPUTS) to improve the prediction accuracy along the Pareto front and in the whole design space. After the first round of robust optimization based on a general metamodel, the training set is updated by simplifying the Pareto solution set. A finite element simulation is performed to construct a high-precision metamodel that combines the kriging and radial basis function (RBF) models to run a new robust optimization. The proposed method was validated by application to the robust optimization of an injection mechanism with a large inner diameter. The results indicated that the SPUTS hybrid metamodel greatly reduced the prediction errors in the test set. The optimized design showed better reliability and robustness and had a greater clearance ratio than the initial design.

This study investigated the effect of laser shock peening (LSP) treatment on the fatigue performance of Q355D steel butt-welded joints. The results demonstrate that LSP sig-nificantly enhances joint fatigue resistance through gradient hardening in surface lay-ers, introduction of high-magnitude residual compressive stress fields, and micro-structural refinement. Specifically, microhardness increased across all joint zones with gradient attenuation of strengthening effects within an approximately 700 μm depth. LSP effectively suppressed residual tensile stress concentration in regions beyond 4 mm on both sides of the weld. Fatigue tests confirmed that LSP substantially extended joint fatigue life: by 113–165% in the high-stress region (250–270 MPa) and 46–63% in the medium-low-stress region (230–240 MPa). Fractographic analysis further revealed reduced fatigue striation spacing and lower microcrack density in LSP-treated speci-mens, reflecting the synergistic effect of residual compressive stress fields and micro-structural refinement in retarding crack propagation. This work substantiates LSP as an effective method for enhancing fatigue resistance in Q355D steel welded joints.

High accuracy and stability in mechanical transmission are crucial for various applications. In spite of the validity of mechanical enhancements, control algorithms’ fulfilment offers a cost-effective and efficient approach to mitigating the effects of noise signals. This study presents a hybrid algorithm that combines EMD with the least mean square (LMS) error to achieve online denoising. Within the algorithm, consecutive mean square error (CMSE) and the 2-norm metric are employed to assess the similarity between intrinsic mode functions (IMFs) and the original signal; therefore, IMFs are separated into three distinct components: noise components, information components, and mixed components. The denoised signal is obtained by partial reconstruction. Subsequently, the denoised signal is employed as a reference signal in the LMS algorithm, which is utilized for practical processing. The performance evaluation of the developed algorithm employs simulation and experimental signals. The obtained results illustrate that the presented approach achieves sufficient accuracy and stability.

Aiming at solving the problems of small fault data samples and insufficient remaining useful life (RUL) prediction accuracy of nuclear power machinery, a method based on an exponential degradation model is proposed to predict the RUL of equipment after the failure warning system alarm. After data preprocessing, time-domain feature extraction, selection, and dimensionality reduction fusion of multiple degradation variables, the exponential degradation model is constructed based on the Bayesian process, and prior information is used. As an application, the RUL of a nuclear power turbine was calculated based on actual monitoring data, the precision curve was used to evaluate the prediction effect, and the RUL prediction results verified the effectiveness of the proposed method.

To quantify the influence of temperature uncertainty on thermal fatigue life prediction of a shot sleeve in an injection mechanism, an uncertainty analysis method based on a Kriging surrogate model and Monte Carlo simulation (MCS) was proposed. The training samples of the surrogate model were obtained by a finite element simulation, and the response relationships between input variables, such as pouring and preheating temperature, and target variables, such as strain and stress, were constructed by the Kriging surrogate model. The input variables were sampled by the MCS, and the predicted stress and strain parameters were combined with the modified universal slope equation to predict the thermal fatigue life of the shot sleeve. The statistical characteristics of the predicted life were obtained. The comparative analysis results indicate that the predicted life considering temperature uncertainty is more accurate than the deterministically predicted value.