scholarly journals Structure-Preserving Algorithms For Sliding Contact Constraint In Director-Based Geometric Exact Beam

Author(s):  
J. Guo ◽  
P. Betsch ◽  
Y. Zhang
2001 ◽  
Vol 29 (3) ◽  
pp. 186-196 ◽  
Author(s):  
X. Yan

Abstract A method is described to predict relative body turn up endurance of radial truck tires using the finite element method. The elastomers in the tire were simulated by incompressible elements for which the nonlinear mechanical properties were described by the Mooney-Rivlin model. The belt, carcass, and bead were modeled by an equivalent orthotropic material model. The contact constraint of a radial tire structure with a flat foundation and rigid rim was treated using the variable constraint method. Three groups of tires with different body turn up heights under inflation and static footprint loading were analyzed by using the finite element method. Based on the detail analysis for stress analysis parameters in the critical regions in the tires, the relative body turn up edge endurance was predicted.


Alloy Digest ◽  
2007 ◽  
Vol 56 (4) ◽  

Abstract TLS A7 Mod. is a modified A7 tool steel that is air hardening and has exceptional wear resistance due to vanadium carbides. It is especially good in sliding contact and often used to handle wet slurries. This datasheet provides information on composition, physical properties, and hardness. It also includes information on wear resistance as well as forming, heat treating, and machining. Filing Code: TS-645. Producer or source: Timken Latrobe Steel.


2001 ◽  
Author(s):  
Frazil Erdogan ◽  
Serkan Dag
Keyword(s):  

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.


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