Publication Type:

Journal Article

Source:

Russian Journal of Non-Ferrous Metals, Pleiades Publishing, Volume 59, Issue 2, p.135-141 (2018)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047552705&doi=10.3103%2fS1067821218020116&partnerID=40&md5=e09bd89c832e61f6ddfc8093b06c17ad

Keywords:

alloy, Artificial Neural Network, AZ91D magnesium alloys, Forecasting, forecasting method, Fuzzy, Fuzzy logic, fuzzy mathematics, magnesium, Magnesium alloys, Neural networks, Pin on disc tribometer, sliding, Soft computing, Soft computing models, Statistical modeling, Sugeno, testing method, Tribological properties, Tribology, Wear, Wear of materials, Wrought magnesium alloys

Abstract:

The wear characteristics of wrought magnesium alloy AZ91D is assessed by varying the wear test parameters namely sliding velocity, sliding distance and normal load in the pin-on-disc tribometer. The experimental results are used to develop a statistical model, and soft computing models based on artificial neural network and Sugeno–Fuzzy logic to predict the wear rate of AZ91D alloy. Sugeno–Fuzzy model had the highest accuracy in prediction and hence used to study the effect of wear test parameters on the wear rate of AZ91D alloy. It is observed that the wear rate increases with decrease in load, increase in sliding velocity, and increase in sliding distance. © 2018, Allerton Press, Inc.

Notes:

cited By 0

Cite this Research Publication

Vaira Vignesh R. and Dr. Padmanaban R., “Forecasting Tribological Properties of Wrought AZ91D Magnesium Alloy Using Soft Computing Model”, Russian Journal of Non-Ferrous Metals, vol. 59, no. 2, pp. 135-141, 2018.