Prediction of Friction Coefficient for Ductile Cast Iron Using Artificial Neural Network Methodology Based on Experimental Investigation

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.