Sliding-Contact Fracture of Brittle Layers

2015 ◽  
pp. 546-607
Keyword(s):  
Sliding Contact ◽  
Contact Fracture ◽  
Brittle Layers

Sliding contact fracture on glass and silicon

1988 ◽  
Vol 23 (8) ◽  
pp. 3006-3014 ◽  
Author(s):  
H. J. Leu ◽  
R. O. Scattergood
Keyword(s):  
Sliding Contact ◽  
Contact Fracture

TLS A7 MODIFIED

Alloy Digest ◽  
2007 ◽  
Vol 56 (4) ◽  
Keyword(s):  
Wear Resistance ◽  
Physical Properties ◽  
Tool Steel ◽  
Heat Treating ◽  
Sliding Contact ◽  
Vanadium Carbides

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.

Cracking of a Graded Half Plane Due to Sliding Contact

2001 ◽  
Author(s):  
Frazil Erdogan ◽  
Serkan Dag
Keyword(s):  
Half Plane ◽  
Sliding Contact

scholarly journals Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4324
Author(s):  
Moaed A. Abd ◽  
Rudy Paul ◽  
Aparna Aravelli ◽  
Ou Bai ◽  
Leonel Lagos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sliding Contact ◽  
Tactile Sensation ◽  
Support Vector ◽  
Textured Surfaces ◽  
K Nearest Neighbor ◽  
Tactile Sensors ◽  
Time Frequency

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

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