Learning-Based Energy Consumption Model of Machining Processes Using Gaussian Process Regression

Alicia Soto Bono, Alicia Soto Bono, Alan McGibney, Susan Rea, Kritchai Witheephanich

2023

Abstract

Currently, the global energy mix is largely dominated by the use of fossil fuels, with the industrial sector accounting for a significant portion of this demand. This results in a significant carbon footprint. As such, the manufacturing industry must become active participants in reducing their impact on the environment through the realization of sustainable manufacturing practices. This study analyzes the performance of a data-driven model enhanced with machine learning techniques in order to build a digital twin that can update its parameters in real-time in response to dynamic changes in the energy consumption of a machining process. This type of model is suitable for the application of a higher-level controller, such as a model predictive controller to optimize the efficiency of the process operation. This paper proposes a digital twin modelling approach based on Gaussian process regression, which updates model parameters with closed-loop data from the process in real-time to retrain the model (evolving). The updating of the model online enables the model to maintain accuracy over time despite changes in the system’s dynamics.

Paper Citation

in Harvard Style

Soto Bono A., McGibney A., Rea S. and Witheephanich K. (2023). Learning-Based Energy Consumption Model of Machining Processes Using Gaussian Process Regression. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 127-134. DOI: 10.5220/0012180100003543

in Bibtex Style

@conference{icinco23,
author={Alicia Soto Bono and Alan McGibney and Susan Rea and Kritchai Witheephanich},
title={Learning-Based Energy Consumption Model of Machining Processes Using Gaussian Process Regression},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2023},
pages={127-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012180100003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Learning-Based Energy Consumption Model of Machining Processes Using Gaussian Process Regression
SN - 978-989-758-670-5
AU - Soto Bono A.
AU - McGibney A.
AU - Rea S.
AU - Witheephanich K.
PY - 2023
SP - 127
EP - 134
DO - 10.5220/0012180100003543
PB - SciTePress