SAGE Publications: Advances in Mechanical Engineering: Table of Contents Table of Contents for Advances in Mechanical Engineering. List of articles from both the latest and ahead of print issues.
- Analysis and simplified model calculation of residual stress in U-rib welding of steel bridge deckpor Lixiong Gu el julio 26, 2024 a las 11:28 am
Advances in Mechanical Engineering, Volume 16, Issue 7, July 2024. <br/>This article examines residual stress generation in U-ribbed steel bridge deck during welding, focusing on post-welding changes in the base material’s residual stress pattern. A thermo-elasto-plastic finite element analysis model was established to conduct numerical simulations of the welding process, summarizing the residual stress changes near the weld seam, the conclusion was drawn that the tensile and compressive forms of transverse residual stress on the upper and lower surfaces of the base plate were inconsistent. To validate the proposed numerical simulation method’s accuracy, the blind hole method was employed to measure welding-induced residual stress, and finite element analysis calculated calibration coefficients A and B, demonstrating the method’s effectiveness. Comparison of experimental measurements with numerical simulation outcomes validate the finite element simulation method’s accuracy and the adopted methodology’s feasibility. Based on these findings, a piecewise linear fitting approach was adopted to develop a simplified model of welding residual stress. The simplified model provides the initial conditions of residual stress for mechanical calculation of U-rib of the same type.
- Rehabilitation robot joint performance evaluation of a zero-spin traction drive with non-Newtonian fluid consideredpor Zou Shuaidong el julio 26, 2024 a las 5:29 am
Advances in Mechanical Engineering, Volume 16, Issue 7, July 2024. <br/>In this study, a zero-spin cone-roller traction drive (CRTD) is presented for the joints transmission system in rehabilitation robots due to its high transmission performance and characteristics of overload protection. It can achieve safe interactions among humans, rehabilitation robots, and the environment, making it a potential substitute for traditional gear-based transmission systems. The performance of CRTD, especially efficiency, is studied in this paper based on an elastohydrodynamic lubrication (EHL) model with the considerations of the non-Newtonian effect. The results demonstrate that the overall efficiency differs in different stages, reaching a maximum value of 95%. The overload protection activates when there is a sharp drop in efficiency, and the overload threshold can be identified by the efficiency, which may provide guidance for operation and optimization.
- Nonlinear modeling and torsional vibration analysis of heavy-duty vehicle powertrain system during accelerationpor Junlong Qu el julio 26, 2024 a las 5:26 am
Advances in Mechanical Engineering, Volume 16, Issue 7, July 2024. <br/>The vehicle acceleration process is often accompanied by torsional vibration of the powertrain system. Poor torsional vibration performance significantly influences the driving comfort of the vehicle and the reliability of powertrain components. Compared to passenger cars, commercial vehicles, especially the heavy-duty truck, exhibit more complicated vibration behaviors during acceleration due to the multiple power branches, various gears, and different working conditions. This article presents systematic research on the modeling method, vibration characteristics, mechanism, and influence factors of the torsional resonance of the heavy-duty vehicle during acceleration. A 16-DOF powertrain model considering multiple nonlinearities of the system is proposed and experimentally validated reliable. Numerical and experimental studies are carried out to investigate the vibration characteristics and mechanism of the heavy-duty vehicle powertrain, and the modal energies and parameter influences are also discussed. Besides, an optimization example is presented to analyze the potential vibration attenuation performance of optimizing the clutch parameters. The results indicate that the overall powertrain mode of the heavy-duty vehicle tends to be aroused by the engine firing frequency during accelerating, inducing violent speed fluctuations of the powertrain components between the clutch and half-shafts. The clutch parameters have significant impacts on the powertrain resonance, and the vibration amplitude of the powertrain system can be effectively attenuated to acceptable levels by optimally designing the clutch parameters.
- Study of spreading, vibration, and fracture behavior of double droplets after positive collisionpor Jinjuan Sun el julio 26, 2024 a las 5:22 am
Advances in Mechanical Engineering, Volume 16, Issue 7, July 2024. <br/>The droplet collision phenomenon is a more complex heat and mass transfer phase transition phenomenon, which is subject to the joint action of kinetics and thermodynamics. During the collision process, the mutual fusion interference of double droplets makes the kinetic mechanism after droplet collision more complicated, and its in-depth study can provide important theoretical support for the fields of engineering applications, industrial production and wetted wall design. In order to investigate the kinetic behavior of double droplets positive collision, this paper mainly combines experimental and numerical simulation methods to investigate the spreading, vibration and fracture characteristics of double droplets of the same volume after collision. Firstly, the rebound vibration of the fused droplet and single droplet is equivalent to a single-degree-of-freedom damped vibration system, and the spreading and vibration characteristics of the single droplet and the double droplets after collision under the same collision velocity are analyzed comparatively by experimental methods. The results show that when the droplet does not fracture, the spreading factor and damping coefficient of single droplet and double droplets gradually increase with the increase of collision velocity, and the vibration time gradually decreases. The damping coefficient and vibration time of the double droplets are higher than that of the single droplet, while the spreading factor is lower than that of the single droplet. Then, the double droplets positive collision phenomenon is studied in depth, and it is found that the spreading factor of the fused droplet increases with the increase of the droplet diameter, the collision velocity, and the wall contact angle. Affected by the low wall temperature, the fused droplet undergoes a phase transition, which affects the bottom flow of the droplet, leading to an increase in the damping coefficient and a decrease in the vibration time. With the decrease of the collision velocity and wall contact angle, the damping coefficient gradually increases and the vibration time decreases. Finally, the numerical simulation method reveals that rebound fracture and spreading fracture phenomena occur after double droplets positive collision, and the critical values of the collision velocity required for the occurrence of rebound fracture and spreading fracture are found. This provides a reliable theoretical basis for the study of the heat and mass transfer process after the collision of multiple droplets on the wall.
- Bayesian neural networks for uncertainty quantification in remaining useful life prediction of systems with sensor monitoringpor Sunday Ochella el julio 26, 2024 a las 5:19 am
Advances in Mechanical Engineering, Volume 16, Issue 7, July 2024. <br/>Many machine learning (ML) algorithms have been developed over the past two decades for prognostics and health management (PHM) of complex engineering systems. However, most of the existing algorithms tend to produce point estimates of a variable of interest, for example the equipment’s remaining useful life (RUL). The point estimation of the RUL often neglects the uncertainty inherent in model parameters and/or the uncertainty associated with data inputs. Bayesian Neural Networks (BNNs) have shown a lot of promise in obtaining credible intervals for model parameters, thus accounting for the uncertainties inherent in both the model and data. This paper proposes a deep BNN model with the Monte Carlo (MC) dropout method to predict the RUL of engineering systems equipped with sensors and monitoring instruments. The model is tested on NASA’s Turbofan Engine Degradation Simulation Dataset and the results are discussed and analyzed. It is revealed that the method can produce highly accurate predictions for RUL distribution parameters in safety critical components.