Influence of cutting parameters in hard turning 40x steel with self-driven rotary tool on surface roughness using genetic programming method and artificial ecosystem-based optimisation

This paper focuses on developing a roughness prediction model based on genetic programming (GP) method and evaluates the influence of cutting parameters (CP) on surface roughness (SR) of 40X steel after heat treatment in rotary tool hard turning process. Different GP models are considered, and the best model is selected for comparison with the multi-variables regression analysis (MRA) model. Next, the optimal value of CP and their influence on SR are determined through artificial ecosystem-based optimisation algorithm. Two best models GP and MRA were used to investigate the effect of CP on SR value with R-2 index higher than 98%. The error value from GP (MSE = 0.014; MAPE = 4.75%) is much smaller than MRA (MSE = 0.045; MAPE = 8.3%). Furthermore, research results show the superiority of GP over MRA in considering the mutual relationship between the input variables for the objective function.