Minimising the machining energy consumption of a machine tool by sequencing the features of a part

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dc.contributor.authorHu, L.
dc.contributor.authorPeng, C.
dc.contributor.authorEvans, S.
dc.contributor.authorPeng, T.
dc.contributor.authorLiu, Y.
dc.contributor.authorTiwari, Ashutosh
dc.date.accessioned2017-02-13T14:36:58Z
dc.date.available2017-02-13T14:36:58Z
dc.date.issued2017-01-11
dc.description.abstractIncreasing energy price and emission reduction requirements are new challenges faced by modern manufacturers. A considerable amount of their energy consumption is attributed to the machining energy consumption of machine tools (MTE), including cutting and non-cutting energy consumption (CE and NCE). The value of MTE is affected by the processing sequence of the features within a specific part because both the cutting and non-cutting plans vary based on different feature sequences. This article aims to understand and characterise the MTE while machining a part. A CE model is developed to bridge the knowledge gap, and two sub-models for specific energy consumption and actual cutting volume are developed. Then, a single objective optimisation problem, minimising the MTE, is introduced. Two optimisation approaches, Depth-First Search (DFS) and Genetic Algorithm (GA), are employed to generate the optimal processing sequence. A case study is conducted, where five parts with 11–15 features are processed on a machining centre. By comparing the experiment results of the two algorithms, GA is recommended for the MTE model. The accuracy of our model achieved 96.25%. 14.13% and 14.00% MTE can be saved using DFS and GA, respectively. Moreover, the case study demonstrated a 20.69% machining time reduction.en_UK
dc.identifier.citationHu L, Peng C, Evans S et al. Minimising the machining energy consumption of a machine tool by sequencing the features of a part, Energy, Volume 121, 15 February 2017, Pages 292–305.en_UK
dc.identifier.issn0360-5442
dc.identifier.urihttp://dx.doi.org/10.1016/j.energy.2017.01.039
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11435
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectMachining energyen_UK
dc.subjectMachine toolsen_UK
dc.subjectFeature sequencingen_UK
dc.subjectCutting volumeen_UK
dc.subjectDepth-First Searchen_UK
dc.subjectGenetic Algorithmen_UK
dc.titleMinimising the machining energy consumption of a machine tool by sequencing the features of a parten_UK
dc.typeArticleen_UK

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