Citation: Khalaf, A.A.; Hanon, M.M.
Prediction of Friction Coefficient for
Ductile Cast Iron Using Artificial
Neural Network Methodology Based
on Experimental Investigation. Appl.
Sci. 2022, 12, 11916. https://doi.org/
10.3390/app122311916
Academic Editors: Rodolfo Haber,
Krzysztof Ejsmont, Aamer
Bilal Asghar and Yong Wang
Received: 20 October 2022
Accepted: 19 November 2022
Published: 22 November 2022
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Article
Prediction of Friction Coefficient for Ductile Cast Iron Using
Artificial Neural Network Methodology Based on
Experimental Investigation
Ahmad A. Khalaf and Muammel M. Hanon *
Middle Technical University (MTU), Baqubah Technical Institute, Baghdad 10074, Iraq
* Correspondence: muammel.m.hanon@mtu.edu.iq
Abstract:
The key objective of the present study is to analyze the friction coefficient and wear
rate for ductile cast iron. Three different microstructures were chosen upon which to perform the
experimental tests under different sliding time, load, and sliding speed conditions. These specimens
were perlite + ferrite, ferrite, and bainitic. Moreover, an artificial neural network (ANN) model
was developed in order to predict the friction coefficient using a set of data collected during the
experiments. The ANN model structure was made up of four input parameters (namely time, load,
number, and nodule diameter) and one output parameter (friction coefficient). The Levenberg–
Marquardt back-propagation algorithm was applied in the ANN model to train the data using
feed-forward back propagation (FFBP). The results of the experiments revealed that the coefficient
of friction reduced as the sliding speed increased under a constant load. Additionally, it exhibits
the same pattern of action when the test is run with a heavy load and constant sliding speed.
Additionally, when the sliding speed increased, the wear rate dropped. The results also show that
the bainite structure is harder and wears less quickly than the ferrite structure. Additionally, the
results pertaining to the ANN structure showed that a single hidden layer model is more accurate
than a double hidden layer model. The highest performance in the validation stage, however, was
observed at epochs 8 and 20, respectively, for a double hidden layer and at 0.012346 for a single layer
at epoch 20.
Keywords: friction coefficient; wear rate; sliding speed; neural network
1. Introduction
The industrial problem of wear can significantly damage the overlapping components
that move by each other [
1
]. There are a number of different factors that can impact the
wear rate, including the metal hardness, sliding time and applied load [
2
]. Moreover, a
number of methods can be used to reduce wear, such as applying lubricants to surfaces or
using cast iron metal as it contains graphite pellets that can lubricate the surfaces of moving
components [
3
]. Many academics have carried out experimental studies to investigate wear
whilst also considering a variety of factors that have been identified in earlier studies [
4
,
5
].
Artificial intelligence (AI) technology has been developed over the last thirty years and has
become one of the most popular ways to predict and overcome various engineering issues
in different fields, particularly in industrial applications [
6
], as it can resolve and predict
nonlinear relationships between the input and output parameters. Meanwhile, of all the AI
machine learning approaches available, the artificial neural network (ANN) is considered
to be one of the most important and popular [7].
ANNs were used to model and optimize various systems in the field of material
science and this is primarily due to their flexibility and ability to understand problems
without needing to know the exact details of the mathematical model. In fact, it does not
even need to have information about the physical conditions. For instance, Radosaw et al.
estimated the tensile strength of ductile iron friction welded joints using hybrid intelligence
Appl. Sci. 2022, 12, 11916. https://doi.org/10.3390/app122311916 https://www.mdpi.com/journal/applsci
