Predictive optimization of surface quality, cost, and energy consumption during milling alloy 2017A: an approach integrating GA-ANN and RSM models

Population growth and economic development are leading to an alarming increase in global energy consumption. Numerically controlled machine tools are widely used in metalworking processes due to their efficiency and ability to achieve high-precision machining. However, the use of simple machining features to determine cutting parameters and the machining process is limited, as parts may contain complex features interacting with each other. This study, therefore, focuses on pocket/groove features and proposes an approach integrating hybrid GA-ANN and RSM algorithms to predict surface quality, cost, and energy consumption (QCE). A parametric study was carried out, taking into account the swarm population size (pop) and the number of neurons (n) in the hidden layer, to find the best prediction using the hybrid GA-ANN algorithm. The results showed the highest correlation values (R2) for all output variables (above 0.97%). The study revealed that modifying machining strategies and sequence planning can significantly decrease energy consumption by as much as 99.25%. Research found that the depth of cut is the most impactful factor on energy use, contributing 60.11%, followed by cost at 46.76%. Additionally, the GA-ANN model demonstrates strong performance in minimizing mean square error (MSE), leading to substantial improvements of 90.91%, 96.55%, and 40.18% in the Etot, Ctot, and Ra parameters when compared to the RSM model. This study highlights the potential of the GA-ANN hybrid approach for multi-criteria prediction (quality, cost, and energy: QCE) in comparison with the RSM method, offering potential improvements for machining 2017A alloy.