Training an Artificial Neural Network Based on Results of the Experiment on Machining of Aluminum Alloys 2196, 2043 and 2099 Used in the Aeronautical Industry

The paper presents a study regarding the tool-life of uncoated and DLC-coated cutting inserts used for machining aluminum–lithium components used in the structure of the Airbus A350 aircraft. The experiment was conducted in an industrial environment that produced aircraft parts, using industrial equipment, under serial processing conditions during 5874 machining hours, resulting in 1440 samples. The experimental results were used as the input data for obtaining predictive models for the estimation of the tool-life machining supervised learning from MATLAB 2025b based on four machine-learning algorithms: trainlm and trainbr (artificial neural networks), fitrtree (decision trees), and fitrensemble (ensemble methods) respectively. The models were evaluated and compared in terms of their performance, which determined the best option. Also, a sensitive analysis of the five predictors was performed. The validation of the four learning algorithms was performed based on a separate set of experimental data, which was not used in learning. The analysis between the experimental results and those predicted by the learning models confirmed their robustness. The analysis between the experimental results and those predicted concluded the best model.