Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine

Liam Morris; Rose Clancy; Andriy Hryshchenko; Dominic O’Sullivan; Ken Bruton

doi:10.1007/978-3-031-19762-8_17

Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine

Liam Morris
, Rose Clancy
, Andriy Hryshchenko
, Dominic O’Sullivan
, Ken Bruton

University College Cork

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

With an ever growing need to reduce energy consumption in the manufacturing industry, process users need to become more aware on how production impacts energy consumption. Computer numerically controlled (CNC) machining tools are a common manufacturing apparatus, and they are known to be energy inefficient. This paper describes the development of an empirical energy consumption model of a CNC with the aim of predicting energy consumption based on the number of parts processed by the machine. The model can then be deployed as part of a decision support (DS) platform, aiding process users to reduce consumption and minimise waste. In using the Calibrated Model Method, the data undergoes initial preparation followed by exploratory data analysis and subsequent model development via iteration. During this analysis, relationships between parameters are explored to find which have the most significant on energy consumption. A training set of 191 datapoints yielded a linear correlation coefficient of 0.95, between the power consumption and total units produced. RMSE, MAPE and MBE validation test yielded results of 0.198, 6.4% and 2.66% respectively.

Original language	English
Title of host publication	Leveraging Applications of Formal Methods, Verification and Validation. Practice - 11th International Symposium, ISoLA 2022, Proceedings
Editors	Tiziana Margaria, Bernhard Steffen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	227-234
Number of pages	8
ISBN (Print)	9783031197611
DOIs	https://doi.org/10.1007/978-3-031-19762-8_17
Publication status	Published - 2022
Event	11th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2022 - Rhodes, Greece Duration: 22 Oct 2022 → 30 Oct 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13704 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2022
Country/Territory	Greece
City	Rhodes
Period	22/10/22 → 30/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Keywords

Calibrated model
CNC
Decision support platform
Digital model
Empirical model
Energy consumption
Linear regression
Machining

Access to Document

10.1007/978-3-031-19762-8_17

Cite this

Morris, L., Clancy, R., Hryshchenko, A., O’Sullivan, D., & Bruton, K. (2022). Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine. In T. Margaria, & B. Steffen (Eds.), Leveraging Applications of Formal Methods, Verification and Validation. Practice - 11th International Symposium, ISoLA 2022, Proceedings (pp. 227-234). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13704 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19762-8_17

Morris, Liam ; Clancy, Rose ; Hryshchenko, Andriy et al. / Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine. Leveraging Applications of Formal Methods, Verification and Validation. Practice - 11th International Symposium, ISoLA 2022, Proceedings. editor / Tiziana Margaria ; Bernhard Steffen. Springer Science and Business Media Deutschland GmbH, 2022. pp. 227-234 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{58f70b9312974e26855322cf002b6324,

title = "Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine",

abstract = "With an ever growing need to reduce energy consumption in the manufacturing industry, process users need to become more aware on how production impacts energy consumption. Computer numerically controlled (CNC) machining tools are a common manufacturing apparatus, and they are known to be energy inefficient. This paper describes the development of an empirical energy consumption model of a CNC with the aim of predicting energy consumption based on the number of parts processed by the machine. The model can then be deployed as part of a decision support (DS) platform, aiding process users to reduce consumption and minimise waste. In using the Calibrated Model Method, the data undergoes initial preparation followed by exploratory data analysis and subsequent model development via iteration. During this analysis, relationships between parameters are explored to find which have the most significant on energy consumption. A training set of 191 datapoints yielded a linear correlation coefficient of 0.95, between the power consumption and total units produced. RMSE, MAPE and MBE validation test yielded results of 0.198, 6.4\% and 2.66\% respectively.",

keywords = "Calibrated model, CNC, Decision support platform, Digital model, Empirical model, Energy consumption, Linear regression, Machining",

author = "Liam Morris and Rose Clancy and Andriy Hryshchenko and Dominic O{\textquoteright}Sullivan and Ken Bruton",

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Morris, L, Clancy, R, Hryshchenko, A, O’Sullivan, D & Bruton, K 2022, Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine. in T Margaria & B Steffen (eds), Leveraging Applications of Formal Methods, Verification and Validation. Practice - 11th International Symposium, ISoLA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13704 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 227-234, 11th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2022, Rhodes, Greece, 22/10/22. https://doi.org/10.1007/978-3-031-19762-8_17

Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine. / Morris, Liam; Clancy, Rose; Hryshchenko, Andriy et al.
Leveraging Applications of Formal Methods, Verification and Validation. Practice - 11th International Symposium, ISoLA 2022, Proceedings. ed. / Tiziana Margaria; Bernhard Steffen. Springer Science and Business Media Deutschland GmbH, 2022. p. 227-234 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13704 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

TY - GEN

T1 - Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine

AU - Morris, Liam

AU - Clancy, Rose

AU - Hryshchenko, Andriy

AU - O’Sullivan, Dominic

AU - Bruton, Ken

PY - 2022

Y1 - 2022

N2 - With an ever growing need to reduce energy consumption in the manufacturing industry, process users need to become more aware on how production impacts energy consumption. Computer numerically controlled (CNC) machining tools are a common manufacturing apparatus, and they are known to be energy inefficient. This paper describes the development of an empirical energy consumption model of a CNC with the aim of predicting energy consumption based on the number of parts processed by the machine. The model can then be deployed as part of a decision support (DS) platform, aiding process users to reduce consumption and minimise waste. In using the Calibrated Model Method, the data undergoes initial preparation followed by exploratory data analysis and subsequent model development via iteration. During this analysis, relationships between parameters are explored to find which have the most significant on energy consumption. A training set of 191 datapoints yielded a linear correlation coefficient of 0.95, between the power consumption and total units produced. RMSE, MAPE and MBE validation test yielded results of 0.198, 6.4% and 2.66% respectively.

AB - With an ever growing need to reduce energy consumption in the manufacturing industry, process users need to become more aware on how production impacts energy consumption. Computer numerically controlled (CNC) machining tools are a common manufacturing apparatus, and they are known to be energy inefficient. This paper describes the development of an empirical energy consumption model of a CNC with the aim of predicting energy consumption based on the number of parts processed by the machine. The model can then be deployed as part of a decision support (DS) platform, aiding process users to reduce consumption and minimise waste. In using the Calibrated Model Method, the data undergoes initial preparation followed by exploratory data analysis and subsequent model development via iteration. During this analysis, relationships between parameters are explored to find which have the most significant on energy consumption. A training set of 191 datapoints yielded a linear correlation coefficient of 0.95, between the power consumption and total units produced. RMSE, MAPE and MBE validation test yielded results of 0.198, 6.4% and 2.66% respectively.

KW - Calibrated model

KW - CNC

KW - Decision support platform

KW - Digital model

KW - Empirical model

KW - Energy consumption

KW - Linear regression

KW - Machining

UR - https://www.scopus.com/pages/publications/85142690180

U2 - 10.1007/978-3-031-19762-8_17

DO - 10.1007/978-3-031-19762-8_17

M3 - Conference proceeding

AN - SCOPUS:85142690180

SN - 9783031197611

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 227

EP - 234

BT - Leveraging Applications of Formal Methods, Verification and Validation. Practice - 11th International Symposium, ISoLA 2022, Proceedings

A2 - Margaria, Tiziana

A2 - Steffen, Bernhard

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2022

Y2 - 22 October 2022 through 30 October 2022

ER -

Morris L, Clancy R, Hryshchenko A, O’Sullivan D , Bruton K. Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine. In Margaria T, Steffen B, editors, Leveraging Applications of Formal Methods, Verification and Validation. Practice - 11th International Symposium, ISoLA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 227-234. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-19762-8_17

Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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