Mass loss prediction of newly developed aluminium-based alloys using artificial neural network

Title:

Mass loss prediction of newly developed aluminium-based alloys using artificial neural network

Creator:

Ramesh Kumar , T. and Rajendran, I.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:e4ad6e7e-4a44-415a-89b9-77b50245bf60
uuid:e4ad6e7e-4a44-415a-89b9-77b50245bf60

Subject:

Mass loss, aluminium-based alloy, sliding wear, and ANN

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The purpose of this study is to predict the mass loss of newly developed aluminium based alloy. Two different alloys are prepared by cladding process and the sliding friction and wear properties of this alloy against high carbon high chromium steel are investigated at different normal loads (50 N, 60 N and 70 N) under different sliding distances. Tests are carried at a constant speed of 1 m/sec under oil lubricated conditions by preheating the circulating engine oil 20w40 at a temperature of 800C. The mass losses are measured and recorded for every interval. An artificial neural network (ANN) model is developed to predict the mass loss of newly developed aluminium-based alloy. It is observed that the predicted values have shown good agreement with experimental values with a correlation coefficient of 0.999973. This model can also be used to predict the mass loss of any material.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2014 Volume:24 | Number:2

Harvested from:

CDK

Metadata only:

false