The development of a data-driven decision support tool to reduce the energy consumption of a manufacturing process

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dc.check.chapterOfThesisThe access option for the thesis is selected as restricted access. The thesis contains propietary information relating to the use case company of the thesis, which is confidential.en
dc.check.date9999-12-31
dc.check.infoRestricted Access
dc.contributor.advisorBruton, Ken
dc.contributor.advisorO'Sullivan, Dominic
dc.contributor.authorMorris, Liamen
dc.contributor.funderHorizon 2020en
dc.date.accessioned2023-06-14T14:04:14Z
dc.date.available2023-06-14T14:04:14Z
dc.date.issued2022-10-07en
dc.date.submitted2022-10-07
dc.description.abstractWith an ever-growing urgency to reduce energy consumption in the manufacturing industry, process stakeholders need more visibility and insights into how much energy they consume, or can expect to consume, for production. In industry today and with the use of Industry 4.0, the way data is utilised has evolved, with data collection and analysis performed digitally. With many long-established manufacturing processes, the jump from older empirical practices to digitalised practices can be difficult. Similarly, many process stakeholders use process data for different means such as production efficiency improvements. From this it can be difficult to ascertain what information is recorded on machines. And with various machines performing varying tasks in part production, this may drive high energy consumption. One such example is computer numerically controlled (CNC) machining tools. These tools are a common manufacturing apparatus and are known to consume energy inefficiently. This thesis describes the development of a hybrid methodology to identify and select key data features on a CNC machine in medical devices manufacturing. Subsequently, this data is used in an empirical energy consumption model of a CNC machine which enables the energy consumption to be determined from the number of parts processed by the machine. In using a calibrated approach, 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 identify which have the most significance on energy consumption. A training set of 191 data points yielded a linear correlation coefficient of 0.95 between the power consumption and total units produced. Root Mean Square Error, Mean Absolute Percentage Error and Mean Bias Error validation tests yielded results of 0.198, 6.4% and 2.66%, respectively.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMorris, L. 2022. The development of a data-driven decision support tool to reduce the energy consumption of a manufacturing process. MSc Thesis, University College Cork.
dc.identifier.endpage99
dc.identifier.urihttps://hdl.handle.net/10468/14585
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::IA/958339/EU/Digital intelligence for collaborative ENergy management in Manufacturing/DENiM
dc.rights© 2022, Liam Morris.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEnergy model
dc.subjectEmpirical model
dc.subjectCalibrated model
dc.subjectCNC
dc.subjectDigital model
dc.subjectLinear regression
dc.subjectEnergy consumption
dc.subjectDecision support platform
dc.titleThe development of a data-driven decision support tool to reduce the energy consumption of a manufacturing process
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc - Master of Scienceen
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