2026-04-07T11:37:06Zhttps://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

Duo, Aitor Reguera-Bakhache, Daniel Izagirre, Unai Aperribay Zubia, Javier 2022-11-23T10:25:43Z 2022-11-23T10:25:43Z 2022 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 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. eng © 2022 IEEE 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 http://purl.org/coar/resource_type/c_c94f