Dynamic Characteristic Analysis of Spur Gear System Considering Tooth Contact State Caused by Shaft Misalignment

Shaft misalignment will change the gear contact state, and then leads to the variation of the internal stiffness excitation of the gear pair, and finally the dynamic characteristics of the gear system will be affected. However, the influence of the gear contact state change on stiffness is usually neglected in the traditional stiffness calculation model for misaligned gears, and the underlying influence mechanism of the gear contact state changes aroused by the shaft misalignment on the dynamic characteristics of gear system is still unclear. To address these shortcomings, traditional loaded tooth contact analysis (LTCA) model is improved with the influences of fillet foundation deformation taken into consideration. Combined with the improved LTCA model, a new mesh stiffness calculation model for misaligned gear considering the tooth contact state is proposed, and then the effects of the contact state changes aroused by the shaft misalignment on the mesh stiffness excitation are studied. Moreover, a dynamic model of misaligned gear system with 8 degree of freedom (DOF) is established, and the dynamic characteristics of the system are simulated and finally verified by experiment. The results show that the proposed model can be used to evaluate the dynamic characteristics of the misaligned gear system with the change of gear tooth contact state taken into consideration. This study provides a theoretical method for the evaluation and identification of the shaft misalignment error.

The Relationship between Social Norms, Avoidance, Future Orientation, and Willingness to Engage in Climate Change Advocacy Communications

This study examined factors associated with willingness to engage in communication behaviors related to climate change advocacy. Data were collected as part of an online, longitudinal US study beginning in March 2020. Outcomes included willingness to post materials online, contact state legislators, and talk with peers about climate change. Covariates included climate change-related social norms, avoidance of climate change information, and perceptions of the future impact of climate change. A minority of the 586 respondents (23%) reported regular conversations about climate change, while approximately half of the respondents reported willingness to discuss climate change with peers (58%), post materials online (47%), and contact state legislators (46%). Strong predictors of willingness to engage in each climate change communications behaviors included climate change social norms, not avoiding climate change information, and believing that climate change will have a negative impact on the future. Findings indicate the importance of designing programs to foster increased climate change communications in order to promote community-level climate change advocacy norms.

Optimization research on arc height of running surface based on reducing partial wear of straddle-type monorail vehicle running wheel tire

This article proposes an ideal of reducing the partial wear of the running wheels by optimizing the arc height of the running surface to improve the wheel-rail contact state. To realize this idea, two kinds of concave and convex running surfaces were designed, the “running wheel-rail beam” finite element model of three kinds of rail surfaces of concave, convex, and plane were established. Taking the arc height of the running surface as the design variable, the total friction work and the friction work deviation (FWD) value as the dual optimization goal, an optimization model of arc height of running surface was established based on finite element model and multidisciplinary optimization platform Modefrontier. An improved genetic algorithm was used and an co-simulation optimization mode was put forward in the optimization. The optimization results show that when the concave height of the inner running surface is 22.62 mm, the total friction work and the FWD values are reduced by 11% and 11.8% respectively; When the convex height of the outer running surface is 11.81 mm, the objection values are reduced by 4.9% and 32.1% respectively. An ideal running surface was obtained and the life of the running wheel was extended by the research.

Alignment Method of Combined Perception for Peg-in-Hole Assembly with Deep Reinforcement Learning

The method of tactile perception can accurately reflect the contact state by collecting force and torque information, but it is not sensitive to the changes in position and posture between assembly objects. The method of visual perception is very sensitive to changes in pose and posture between assembled objects, but they cannot accurately reflect the contact state, especially since the objects are occluded from each other. The robot will perceive the environment more accurately if visual and tactile perception can be combined. Therefore, this paper proposes the alignment method of combined perception for the peg-in-hole assembly with self-supervised deep reinforcement learning. The agent first observes the environment through visual sensors and then predicts the action of the alignment adjustment based on the visual feature of the contact state. Subsequently, the agent judges the contact state based on the force and torque information collected by the force/torque sensor. And the action of the alignment adjustment is selected according to the contact state and used as a visual prediction label. Whereafter, the network of visual perception performs backpropagation to correct the network weights according to the visual prediction label. Finally, the agent will have learned the alignment skill of combined perception with the increase of iterative training. The robot system is built based on CoppeliaSim for simulation training and testing. The simulation results show that the method of combined perception has higher assembly efficiency than single perception.

Tactile Perception for Teleoperated Robotic Exploration within Granular Media

The sense of touch is essential for locating buried objects when vision-based approaches are limited. We present an approach for tactile perception when sensorized robot fingertips are used to directly interact with granular media particles in teleoperated systems. We evaluate the effects of linear and nonlinear classifier model architectures and three tactile sensor modalities (vibration, internal fluid pressure, fingerpad deformation) on the accuracy of estimates of fingertip contact state. We propose an architecture called the Sparse-Fusion Recurrent Neural Network (SF-RNN) in which sparse features are autonomously extracted prior to fusing multimodal tactile data in a fully connected RNN input layer. The multimodal SF-RNN model achieved 98.7% test accuracy and was robust to modest variations in granular media type and particle size, fingertip orientation, fingertip speed, and object location. Fingerpad deformation was the most informative modality for haptic exploration within granular media while vibration and internal fluid pressure provided additional information with appropriate signal processing. We introduce a real-time visualization of tactile percepts for remote exploration by constructing a belief map that combines probabilistic contact state estimates and fingertip location. The belief map visualizes the probability of an object being buried in the search region and could be used for planning.

Experimental Verification of Contact Acoustic Nonlinearity at Rough Contact Interfaces

When a longitudinal wave passes through a contact interface, second harmonic components are generated due to contact acoustic nonlinearity (CAN). The magnitude of the generated second harmonic is related to the contact state of the interface, of which a model has been developed using linear and nonlinear interfacial stiffness. However, this model has not been sufficiently verified experimentally for the case where the interface has a rough surface. The present study verifies this model through experiments using rough interfaces. To do this, four sets of specimens with different interface roughness values (Ra = 0.179 to 4.524 μm) were tested; one set consists of two Al6061-T6 blocks facing each other. The second harmonic component of the transmitted signal was analyzed while pressing on both sides of the specimen set to change the contact state of the interface. The experimental results showed good agreement with the theoretical prediction on the rough interface. The magnitude of the second harmonic was maximized at a specific contact pressure. As the roughness of the contact surface increased, the second harmonic was maximized at a higher contact pressure. The location of this maximal point was consistent between experiments and theory. In this study, an FEM simulation was conducted in parallel and showed good agreement with the theoretical results. Thus, the developed FEM model allows parametric studies on various states of contact interfaces.