###### 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 perfile + 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 bainitic 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.  
friction coefficient; wear rate; sliding speed; neural network +
Footnote †: journal: Applied Sciences  
methods [8]. The primary goal of their work was to use support vector regression (SVR), genetic algorithms (GA) and an imperialist competitive approach to maximize the welding parameters (ICA). The findings demonstrated that the TS of 256.93 MPa was achieved using SVR plus GA algorithms with heating force welding parameters of 40 kN, a heating time of 300 s, and an upsetting force of 10.12 kN. In addition, the use of hybrid intelligent approaches enhanced the TS joints from 211 to 258 mpa for the ZT-14 type friction welder. Similarly, [PERSON] et al. used an ANN approach incorporating the Levenberg-Marquardt algorithm to predict the tensile strength of friction stir welded (FSW) for the AA1100 aluminum alloy [9]. Rotation speed, welding speed, shoulder diameter, and pin diameter were all applied as the ANN model's input parameters. The findings for the projected TS show that the ANN model is effective in predicting the tensile stress when these input parameters are present. The mechanical characteristics of ductile cast iron were identified by [PERSON] et al., utilizing ANN based on the error back-propagation training technique [10]. The multilayer hidden layers of the ANN model had sigmoid activation functions. The 13 weights of chemical elements in melt were the input parameters used in the ANN model. Subsequently, the output included tensile and yield strength, hardness, and elongation. Additionally, [PERSON] et al. built and trained a variety of ANN models to predict the quality of ductile cast iron and the findings indicated that ANN models are effective for use in melt shop on-line production control [11]. Furthermore, [PERSON] et al. estimated the friction welding process parameters of Incoloy 800H joints and correlated the input and output responses of the friction welding using a hybrid ANN method incorporating the optimization algorithm [12]. It also explored how the best strength and hardness of joints can be achieved with the shortest burn-off length. The findings indicated that the friction welding process parameters may be accurately predicted using an ANN model combined with a genetic algorithm. Meanwhile, the friction coefficient and wear rate were examined in [PERSON] et al. 's study using an ANN model under the influence of various loads, pin heating temperature, and speed.