IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i3d10.1007_s10845-018-1440-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-018-1440-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-018-1440-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu Tan & Lining Xing & Zhaoquan Cai & Gaige Wang, 2020. "Analysis of production cycle-time distribution with a big-data approach," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1889-1897, December.
    2. 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.
    3. 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).
    4. 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.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Shih-Pin, 2004. "Parametric nonlinear programming for analyzing fuzzy queues with finite capacity," European Journal of Operational Research, Elsevier, vol. 157(2), pages 429-438, September.
    2. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    3. Maximilian Zarte & Agnes Pechmann & Isabel L. Nunes, 2022. "Problems, Needs, and Challenges of a Sustainability-Based Production Planning," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    4. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    5. Wang, Di & He, Bin & Hu, Zhimu, 2024. "Financial technology and firm productivity: Evidence from Chinese listed enterprises," Finance Research Letters, Elsevier, vol. 63(C).
    6. Zhiyuan Fu & Ghulam Rasool Madni, 2024. "Unveiling the affecting mechanism of digital transformation on total factor productivity of Chinese firms," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-23, February.
    7. Wang, Linhui & Chen, Qi & Dong, Zhiqing & Cheng, Lu, 2024. "The role of industrial intelligence in peaking carbon emissions in China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    8. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
    9. Seon Han Choi & Byeong Soo Kim, 2025. "Intelligent factory layout design framework through collaboration between optimization, simulation, and digital twin," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1547-1561, March.
    10. Shiguang Li & Yixiang Tian, 2023. "How Does Digital Transformation Affect Total Factor Productivity: Firm-Level Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    11. Muhammad Hassan & Marcus Svadling & Niclas Björsell, 2024. "Experience from implementing digital twins for maintenance in industrial processes," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 875-884, February.
    12. Jingbo Liu & Fan Jiang & Shinichi Tashiro & Shujun Chen & Manabu Tanaka, 2025. "A physics-informed and data-driven framework for robotic welding in manufacturing," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
    13. Wei Fang & Lianyu Zheng, 2020. "Shop floor data-driven spatial–temporal verification for manual assembly planning," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1003-1018, April.
    14. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    15. Zhe Li & Yi Wang & Kesheng Wang, 2020. "A data-driven method based on deep belief networks for backlash error prediction in machining centers," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1693-1705, October.
    16. Chaohong Na & Xue Chen & Xiaojun Li & Yuting Li & Xiaolan Wang, 2022. "Digital Transformation of Value Chains and CSR Performance," Sustainability, MDPI, vol. 14(16), pages 1-24, August.
    17. Mario Vozza & Joseph Polden & Giulio Mattera & Gianfranco Piscopo & Silvestro Vespoli & Luigi Nele, 2024. "Explaining the Anomaly Detection in Additive Manufacturing via Boosting Models and Frequency Analysis," Mathematics, MDPI, vol. 12(21), pages 1-17, October.
    18. Xifan Yao & Nanfeng Ma & Jianming Zhang & Kesai Wang & Erfu Yang & Maurizio Faccio, 2024. "Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 235-255, January.
    19. Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
    20. Ho, G.T.S. & Tang, Yuk Ming & Leung, Eric K.H. & Tong, P.H., 2025. "Integrated reinforcement learning of automated guided vehicles dynamic path planning for smart logistics and operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 196(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-018-1440-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.