NEURAL NETWORKS FOR SIMULATED MICRO-FEATURE EVALUATION AND CLASSIFICATION

Abstract

In recent years, advanced manufacturing has gained traction, particularly in the microscale industries, to enhance material properties. The creation of micro-textures and achieving precise accuracy at the microscale are crucial for the performance of modern devices. In this paper, a method of facilitating these measures will be presented with the integration of Artificial Neural Networks (ANNs) to find the nonlinear relationship between the machining input parameters and the output microstructures’ geometrical shapes. GoogleNet, a kind of Convolutional Neural Networks (CNNs), was also used to categorize the images between defective and ideal dimples to reach optimum precision in the manufacturing context.