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A big data analytics based machining optimisation approach

Author

Listed:
  • Wei Ji

    (KTH Royal Institute of Technology
    Harbin University of Science and Technology)

  • Shubin Yin

    (Harbin University of Science and Technology)

  • Lihui Wang

    (KTH Royal Institute of Technology)

Abstract

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.

Suggested Citation

  • Wei Ji & Shubin Yin & Lihui Wang, 2019. "A big data analytics based machining optimisation approach," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1483-1495, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-018-1440-9
    DOI: 10.1007/s10845-018-1440-9
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    References listed on IDEAS

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    1. Bretthauer, Kurt M. & Cote, Murray J., 1997. "Nonlinear programming for multiperiod capacity planning in a manufacturing system," European Journal of Operational Research, Elsevier, vol. 96(1), pages 167-179, January.
    2. Yeo, S. H., 1995. "A multipass optimization strategy for CNC lathe operations," International Journal of Production Economics, Elsevier, vol. 40(2-3), pages 209-218, August.
    3. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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    Cited by:

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    2. Hendrik Hotz & Benjamin Kirsch & Jan C. Aurich, 2021. "Impact of the thermomechanical load on subsurface phase transformations during cryogenic turning of metastable austenitic steels," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 877-894, March.
    3. Marie-Anne Le-Dain & Lamiae Benhayoun & Judy Matthews & Marine Liard, 2023. "Barriers and opportunities of digital servitization for SMEs: the effect of smart Product-Service System business models," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 359-393, March.
    4. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Siti Nurfadilah Binti Jaini & Deug-Woo Lee & Seung-Jun Lee & Mi-Ru Kim & Gil-Ho Son, 2021. "Indirect tool monitoring in drilling based on gap sensor signal and multilayer perceptron feed forward neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1605-1619, August.
    6. Gautam Dutta & Ravinder Kumar & Rahul Sindhwani & Rajesh Kr. Singh, 2021. "Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1679-1698, August.
    7. Pulin Li & Kai Cheng & Pingyu Jiang & Kanet Katchasuwanmanee, 2022. "Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 103-119, January.
    8. Huidong Sun & Mustafa Raza Rabbani & Muhammad Safdar Sial & Siming Yu & José António Filipe & Jacob Cherian, 2020. "Identifying Big Data’s Opportunities, Challenges, and Implications in Finance," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    9. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    10. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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