2026-04-07T10:03:49Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/58792026-02-24T09:13:53Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Duo, Aitor author Reguera-Bakhache, Daniel author Izagirre, Unai author Aperribay Zubia, Javier author 2022 In the context of Industry 4.0, the optimization of manufacturing processes is a challenge. Although in recent years many of the efforts have been in this direction, there is still improvement opportunities in these processes. The optimisation of the power consumed by the processes can be improved by means of the parameters of control. To date, this challenge has been addressed by Multi-Objective optimization techniques, however, Reinforcement Learning based approaches are raising with promising results in many industrial fields.In this paper, we propose a Reinforcement Learning (RL) based approach to optimize the active power consumption of a machining process by the cutting conditions selection. Through the application of Q-Learning algorithm, the agent self-learns the optimal solution through interacting with the environment. The approach was validated in three different scenarios demonstrating the feasibility of RL application to determine the cutting conditions values in order to optimize the active power consumption. 978-1-6654-9996-5 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168355 https://hdl.handle.net/20.500.11984/5879 Q-learning Power demand Manufacturing processes Process control Turning Fourth Industrial Revolution optimization Active Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approach