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A big data analytics based machining optimisation approach
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0002-9642-6983
Harbin Univ Sci & Technol, Dept Mech Engn, Harbin, Heilongjiang, Peoples R China..
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-8679-8049
2019 (English)In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145, Vol. 30, no 3, p. 1483-1495Article in journal (Refereed) Published
Abstract [en]

Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.

Place, publisher, year, edition, pages
SPRINGER , 2019. Vol. 30, no 3, p. 1483-1495
Keywords [en]
Big data analytics, Machining optimisation, Hybrid algorithm, Deep belief network, Genetic algorithm
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-246246DOI: 10.1007/s10845-018-1440-9ISI: 000459423700032Scopus ID: 2-s2.0-85050695013OAI: oai:DiVA.org:kth-246246DiVA, id: diva2:1302069
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-06-11Bibliographically approved

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Ji, WeiWang, Lihui

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