Performance evaluation of brake pads developed using two different manufacturing methods

Saindane, Udaykumar V.; Soni, Sandeep; Menghani, Jyoti

doi:10.1002/mawe.202100407

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nanocomposites and advanced materials have a widespread application in various fields, and based on the production methods, there are different generation strategies [1][2][3]. Adding alloying elements to the copper lattice is a common way to increase the mechanical properties alongside the electrical properties of copperbased composites.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hardness of Copper-based Nanocomposites Fabricated by Ball-milling using A Rtificial Neural Network

Babaheydari,

Mirabootalebi,

Akbari

et al. 2023

IJE

View full text Add to dashboard Cite

Copper-based alloys are one of the most popular materials in the power distribution, welding industry, hydraulic equipment, industrial machinery, etc. Among different methods for the fabrication of Cu alloys, mechanical alloying (MA) is the major approach due to the fact that this approach is simple, inexpensive, suitable for mass production, and has a high capacity for homogeneous distribution of the second phase. However, the prediction of the hardness of products is very difficult in MA because of a lot of effective parameters. In this work, we designed a feed-forward back propagation neural network (FFBPNN) to predict the hardness of copper-based nanocomposites. First, some of the most common nanocomposites of copper including Cu-Al, Cu-Al2O3, Cu-Cr, and Cu-Ti were synthesized by mechanical alloying of copper at varying weight percentages (1, 3, and 6). Next, the alloyed powders were compacted by a cold press (12 tons) and subjected to heat treatment at 650˚C. Then, the strength of the alloys was measured by the Vickers microscopy test. Finally, to anticipate the micro-hardness of Cu nanocomposites, the significant variables in the ball milling process including hardness, size, and volume of the reinforcement material, vial speed, the ball-to-powder-weight-ratio (BPR), and milling time; were determined as the inputs, and hardness of nanocomposite was assumed as an output of the artificial neural network (ANN). For training the ANN, many different ANN architectures have been employed and the optimal structure of the model was obtained by regression of 0.9914. The network was designed with two hidden layers. The first and second hidden layer includes 12 and 8 neurons, respectively. The comparison between the predicted results of the network and the experimental values showed that the proposed model with a root mean square error (RMSE) of 3.7 % can predict the microhardness of the nanocomposites.

show abstract

Section: Introductionmentioning

confidence: 99%