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Active Power Optimization of a Turning Process by Cutting Conditions Selection A Q-Learning Approach.pdf (746.7Kb)
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Título
Active Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approach
Autor-a
Duo, Aitor ccMondragon Unibertsitatea
Reguera-Bakhache, Daniel ccMondragon Unibertsitatea
Izagirre, Unai ccMondragon Unibertsitatea
Aperribay Zubia, Javier ccMondragon Unibertsitatea
Grupo de investigación
Análisis de datos y ciberseguridad
Mecanizado de alto rendimiento
Fecha de publicación
2022
Editor
IEEE
Palabras clave
Q-learning
Power demand
Manufacturing processes
Process control ... [+]
Q-learning
Power demand
Manufacturing processes
Process control
Turning
Fourth Industrial Revolution
optimization [-]
Resumen
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 opportu ... [+]
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. [-]
URI
https://hdl.handle.net/20.500.11984/5879
Versión del editor
https://doi.org/10.1109/ETFA52439.2022.9921714
ISBN
978-1-6654-9996-5
Publicado en
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)  6-9 septiembre, Stuttgart. Pp. 1-6
Tipo de documento
Contribución a congreso
Versión
Postprint – Accepted Manuscript
Derechos
© 2022 IEEE
Acceso
Acceso embargado (hasta 2024-10-31)
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  • Congresos - Ingeniería [242]

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Recolectado por:

OpenAIREBASE

Validado por:

OpenAIRERebiun
MONDRAGON UNIBERTSITATEA | Biblioteca
Contacto | Sugerencias
DSpace