A Neural Model of Friction Material Behaviour University of Belgrade, Serbia

Format:

Conference/Event

Author/Creator:

Aleksendrić, Dragan, author.

Conference Name:

24th Annual Brake Colloquium and Exhibition (2006-10-08 : Grapevine, Texas, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2006

Summary:

The neural computation ability to model complex non-linear relationships directly from experimental data, without any prior assumptions about nature of the input/output relationships, has been used in this paper. An artificial neural network technique was used to develop a neural model for predicting the friction materials behavior under prescribed testing conditions. By means of neural modeling of the friction materials behavior, the relationship between 26 input parameters and one output parameter (brake factor C) has been established. The input parameters are defined by the friction material formulation (18 parameters), manufacturing conditions (5 parameters), and testing conditions (3 parameters). Prediction abilities of the neural model have been evaluated by comparison the real cold performance obtained during friction material testing on the single end full-scale inertia dynamometer and predicted ones

Notes:

Vendor supplied data

Publisher Number:

2006-01-3200

Access Restriction:

Restricted for use by site license