Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques

Abstract

RBF) neural network were trained to estimate the diameter of machined holes. The multisensory approach includes an acoustic emission sensor, accelerometer, dynamometer and an electric power sensor. The optimum configuration for each artificial intelligence system was determined based on algorithms designed to examine the influence of each system's signals and specific parameters on the final result of the estimate. The results indicated the MLP ANN was more robust in withstanding data variation. The ANFIS system and RBF network showed markedly varying results in response to variations in the obtained data during training, suggesting these systems should always be trained with the dataset presented in the same order. A satisfactory response between the multisensory approach and MLP network was observed. The vertical component of force, along the z axis, was the only parameter able to present valid results for all the artificial intelligence systems analysed.

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Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

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