Tehranizadeh, FarazEbrahimi Araghizad,A.Tehranizadeh,F.Pashmforoush,F.Budak E., (1),2024-06-232024-06-23202400007-8506https://doi.org/10.1016/j.cirp.2024.04.083https://hdl.handle.net/20.500.12469/5880This 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. © 2024 CIRPeninfo:eu-repo/semantics/closedAccessMachine learningMillingMonitoringArticle10.1016/j.cirp.2024.04.0832-s2.0-85194108205Q2Q1