Using model selection and reduction to develop an empirical model to predict energy consumption of a CNC machine

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dc.contributor.authorMorris, Liamen
dc.contributor.authorClancy, Roseen
dc.contributor.authorHryshchenko, Andriyen
dc.contributor.authorO’Sullivan, Dominicen
dc.contributor.authorBruton, Kenen
dc.contributor.editorMargaria, Tizianaen
dc.contributor.editorSteffen, Bernharden
dc.contributor.funderHorizon 2020en
dc.date.accessioned2023-05-02T13:40:58Z
dc.date.available2023-05-02T13:40:58Z
dc.date.issued2022-10-17en
dc.description.abstractWith 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.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMorris, L., Clancy, R., Hryshchenko, A., O’Sullivan, D. and Bruton, K. (2022) 'Using model selection and reduction to develop an empirical model to predict energy consumption of a CNC machine', in Margaria, T. and Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Practice. ISoLA 2022. Lecture Notes in Computer Science, 13704, pp. 227-234. Springer, Cham. doi: 10.1007/978-3-031-19762-8_17en
dc.identifier.doi10.1007/978-3-031-19762-8_17en
dc.identifier.eissn1611-3349en
dc.identifier.endpage234en
dc.identifier.isbn9783031197611en
dc.identifier.isbn9783031197628en
dc.identifier.issn0302-9743en
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage227en
dc.identifier.urihttps://hdl.handle.net/10468/14421
dc.identifier.volume13704en
dc.language.isoenen
dc.publisherSpringer Nature Switzerland AGen
dc.relation.ispartofLecture Notes in Computer Scienceen
dc.relation.ispartofISoLA 2022: International Symposium on Leveraging Applications of Formal Methodsen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::IA/958339/EU/Digital intelligence for collaborative ENergy management in Manufacturing/DENiMen
dc.rights© 2022, the Authors, under exclusive licence to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of a paper published as: Morris, L., Clancy, R., Hryshchenko, A., O’Sullivan, D. and Bruton, K. (2022) 'Using model selection and reduction to develop an empirical model to predict energy consumption of a CNC machine', in Margaria, T. and Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Practice. ISoLA 2022. Lecture Notes in Computer Science, 13704, pp. 227-234. Springer, Cham, doi: 10.1007/978-3-031-19762-8_17. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-19762-8_17en
dc.subjectEmpirical modelen
dc.subjectCalibrated modelen
dc.subjectCNC Machining Digital modelen
dc.subjectLinear regressionen
dc.subjectEnergy consumptionen
dc.subjectDecision support platformen
dc.titleUsing model selection and reduction to develop an empirical model to predict energy consumption of a CNC machineen
dc.typeConference itemen
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