where 𝑝 0 , 𝑝 𝑖 , 𝑝 𝑖𝑖 , 𝑝 𝑖𝑗 are constants, 𝑥 𝑖 , 𝑥 𝑗 are independent input parameters and 𝑦 𝑚 is output parameters. PR employs the method of least squares to minimize variance in coefficient estimation and reduce the loss between actual and predicted values. However, in machining CFRP composites, where relationships are complex and non-linear, PR often requires high polynomial degrees, making it more sensitive to outliers and prone to overfitting . is one of the most commonly utilised machine learning model . Among the existing ANNs, MLP (also known as Back-propagation neural network -BPNN), is a typical neural network which consists of an input layer, one or more hidden layers and an output layer, see Fig. ). The value of each neuron in both the hidden and output layers is determined by the previous layer's neuron and its associated weights :