Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7003869 | Wear | 2018 | 23 Pages |
Abstract
The results demonstrate that the neural network can be used efficiently to predict wheel and rail wear. The methods of collecting wear data using the replica material and the profilometer have also proved effective for wheel and rail wear measurements for training and validating the neural network. The laboratory tests have aimed to validate the wear predictions for realistic wheel and rail profiles and materials but they necessarily cover only a limited set of conditions. The next steps for this work will be to test the methods for rail and wheel data from field tests.
Keywords
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Colloid and Surface Chemistry
Authors
A. Shebani, S. Iwnicki,