Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Table 1

Coefficient of friction optimization models.

Type name	Chosen factors
Artificial neural network	Activation function: tan h, α = 0.013, hidden layers = (10,10,10)
k-Nearest neighbor	Number of considered neighbors = 6, weights = “uniform”
Random forest	Maximum features = 5, n_estimators = 80
Support vector machine	Kernel = RBF, γ = 0.09, C = 100
Gradient boosting machine	Learning rate = 0.9, maximum-depth = 3, n_estimator = 150

RBF, radial basis function.