Araghizad, Arash EbrahimiTehranizadeh, FarazPashmforoush, FarzadBudak, Erhan2024-06-232024-06-23202400007-85061726-0604https://doi.org/10.1016/j.cirp.2024.04.083Pashmforoush, Farzad/0000-0002-2219-5158; Ebrahimi Araghizad, Arash/0000-0003-4117-1773This study addresses the critical need for intelligent process monitoring in unmanned manufacturing through real-time fault detection. The proposed hybrid approach, which is focused on overcoming the limitations of existing methods, utilizes machine learning (ML) for precise parameter identification in real-time to detect deviations. The ML system is developed using extensive data obtained from simulations based on enhanced force models also achieved through ML. Demonstrating over 96 % accuracy in real-time predictions, the method proves applicable for diverse unmanned manufacturing applications, including monitoring and process optimization, emphasizing its adaptability for industrial implementation using CNC controller signals. (c) 2024 CIRP. Published by Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessMillingMonitoringMachine learningArticle325328173WOS:00127695040000110.1016/j.cirp.2024.04.0832-s2.0-85194108205Q2Q1