Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks
Yakub Iqbal Mogul, Ibtisam Mogul, Jaimon Dennis Quadros, Ma Mohin, Abdul Aabid, Muneer Baig, Mohammad Abdul Malik
Abstract
• Developed a back propagation neural network model to predict the depth of cut in abrasive water jet machining of Ti-6AL-4V aluminum alloy. • Analyzed the impact of five key parameters: water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size, and nozzle-to-orifice diameter. • Utilized the L27 Taguchi-design of experiments to structure and validate the experimental approach. • Achieved up to 95 % accuracy in predicting experimental depth of cut values using the neural network model. • Demonstrated consistency between predicted and experimental results, validating the algorithm's effectiveness. The current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. The study analyzed depth of cut for five different water jet abrasive parameters namely, water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size, and nozzle to orifice diameter. Experiments were conducted as per the L 27 Taguchi-design of experiments (DoE). The back propagation neural network model comprising of one input layer, one hidden layer and an output layer with an architecture of 1–5–6 was chosen for conducting the analysis. The algorithm predicted the Taguchi based output values for the experimental depth of cut with an accuracy of up to 95 %. The neural network algorithm further automated itself, generating 50 new data sets for K-cross validation, out of which 70 %, 20 %, and 10 % of the data were used for training, testing, and validation, respectively. Confirmatory experiments were conducted for depth of cut and assessed against the data set used for validation (10 %). The results showed that as the depth of cut was small, i.e., ranging from 3 mm to 5 mm, the algorithm was unable to predict the optimized parameters, however, the prediction improved as the depth of cut increased. Overall, the consistency between the neural network predicted and the experimental depth of cut throughout the algorithm confirmed the validity of the procedure and the appropriateness of the algorithm.