Design of an intelligent simulator ANN and ANFIS model in the prediction of milling performance (QCE) of alloy 2017A

Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) are machine learning techniques that enable modeling and prediction of various properties in the milling process of alloy 2017A, including quality, cost, and energy consumption (QCE). To utilize ANNs or ANFIS for QCE prediction, researchers must gather a dataset consisting of input-output pairs that establish the relationship between QCE and various input variables such as machining parameters, tool properties, and material characteristics. Subsequently, this dataset can be employed to train a machine learning model using techniques like backpropagation or gradient descent. Once the model has been trained, predictions can be made on new input data by providing the desired input variables, resulting in predicted QCE values as output. This study comprehensively examines and identifies the scientific contributions of strategies, machining sequences, and cutting parameters on surface quality, machining cost, and energy consumption using artificial intelligence (ANN and ANFIS). The findings indicate that the optimal neural architecture for ANNs, utilizing the Bayesian regularization (BR) algorithm, is a {3-10-3} architecture with an overall mean square error (MSE) of 2.74 x 10-3. Similarly, for ANFIS, the optimal structure yielding better error and correlation for the three output variables (Etot, Ctot, and Ra) is a {2, 2, 2} structure. The results demonstrate that using the BR algorithm with a multi-criteria output response yields favorable outcomes compared to the ANFIS.