IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i1d10.1007_s10845-020-01654-2.html
   My bibliography  Save this article

Approach to derive golden paths based on machine sequence patterns in multistage manufacturing process

Author

Listed:
  • Chang-Ho Lee

    (Pohang University of Science and Technology)

  • Dong-Hee Lee

    (Hanyang University)

  • Young-Mok Bae

    (SK Hynix)

  • Seung-Hyun Choi

    (Pohang University of Science and Technology)

  • Ki-Hun Kim

    (Ulsan National Institute of Science and Technology (UNIST)
    Delft University of Technology)

  • Kwang-Jae Kim

    (Pohang University of Science and Technology)

Abstract

A multistage manufacturing process (MMP) consists of several consecutive process stages, each of which has multiple machines performing the same functions in parallel. A manufacturing path (simply referred to as path) is defined as an ordered set indicating a record of machines assigned to a product at each process stage of an MMP. An MMP usually produces products through various paths. In practice, multiple machines in a process stage have different operational performances, which accumulate during production and affect the quality of products. This study proposes a heuristic approach to derive the golden paths that produce products whose quality exceeds the desired level. The proposed approach consists of the searching phase and the merging phase. The searching phase extracts two types of machine sequence patterns (MSPs) from a path dataset in an MMP. An MSP is a subset of the path that is defined as an ordered set of assigned machines from several process stages. The two extracted types of MSPs are: (1) superior MSP, which affects the production of superior-quality products, and (2) inferior MSP, which affects the production of inferior-quality products, called inferior MSP. The merging phase derives the golden paths by combining superior MSPs and excluding inferior MSPs. The proposed approach is verified by applying it to a hypothetical path dataset and the semiconductor tool fault isolation (SETFI) dataset. This verification shows that the proposed approach derives the golden paths that exceed the predefined product quality level. This outcome demonstrates the practical viability of the proposed approach in an MMP.

Suggested Citation

  • Chang-Ho Lee & Dong-Hee Lee & Young-Mok Bae & Seung-Hyun Choi & Ki-Hun Kim & Kwang-Jae Kim, 2022. "Approach to derive golden paths based on machine sequence patterns in multistage manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 167-183, January.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-020-01654-2
    DOI: 10.1007/s10845-020-01654-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01654-2
    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/s10845-020-01654-2?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Chayma Sellami & Carlos Miranda & Ahmed Samet & Mohamed Anis Bach Tobji & François de Beuvron, 2020. "On mining frequent chronicles for machine failure prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1019-1035, April.
    2. Bera, Sasadhar & Mukherjee, Indrajit, 2016. "A multistage and multiple response optimization approach for serial manufacturing system," European Journal of Operational Research, Elsevier, vol. 248(2), pages 444-452.
    3. Ran Jin & Kaibo Liu, 2013. "Multimode variation modeling and process monitoring for serial-parallel multistage manufacturing processes," IISE Transactions, Taylor & Francis Journals, vol. 45(6), pages 617-629.
    4. Feng Ju & Jingshan Li & Guoxian Xiao & Jorge Arinez & Weiwen Deng, 2015. "Modeling, analysis, and improvement of integrated productivity and quality system in battery manufacturing," IISE Transactions, Taylor & Francis Journals, vol. 47(12), pages 1313-1328, December.
    5. Shichang Du & Xufeng Yao & Delin Huang, 2015. "Engineering model-based Bayesian monitoring of ramp-up phase of multistage manufacturing process," International Journal of Production Research, Taylor & Francis Journals, vol. 53(15), pages 4594-4613, August.
    6. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    7. Foivos Psarommatis & Gökan May & Paul-Arthur Dreyfus & Dimitris Kiritsis, 2020. "Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 1-17, January.
    Full references (including those not matched with items on IDEAS)

    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. Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
    2. Christoph March & Ina Schieferdecker, 2021. "Technological Sovereignty as Ability, Not Autarky," CESifo Working Paper Series 9139, CESifo.
    3. Metzker Soares, Paula & Thevenin, Simon & Adulyasak, Yossiri & Dolgui, Alexandre, 2024. "Adaptive robust optimization for lot-sizing under yield uncertainty," European Journal of Operational Research, Elsevier, vol. 313(2), pages 513-526.
    4. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    5. Pompeu Casanovas & Louis de Koker & Mustafa Hashmi, 2022. "Law, Socio-Legal Governance, the Internet of Things, and Industry 4.0: A Middle-Out/Inside-Out Approach," J, MDPI, vol. 5(1), pages 1-28, January.
    6. Anna Kwiotkowska & Radosław Wolniak & Bożena Gajdzik & Magdalena Gębczyńska, 2022. "Configurational Paths of Leadership Competency Shortages and 4.0 Leadership Effectiveness: An fs/QCA Study," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    7. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    8. Bariş Keçeci & Yusuf Tansel Iç & Ergün Eraslan, 2019. "Development of a Spreadsheet DSS for Multi-Response Taguchi Parameter Optimization Problems Using the TOPSIS, VIKOR, and GRA Methods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1501-1531, September.
    9. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).
    10. Iñigo Flores Ituarte & Suraj Panicker & Hari P. N. Nagarajan & Eric Coatanea & David W. Rosen, 2023. "Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 219-241, January.
    11. Qinglan Liu & Adriana Hofmann Trevisan & Miying Yang & Janaina Mascarenhas, 2022. "A framework of digital technologies for the circular economy: Digital functions and mechanisms," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2171-2192, July.
    12. Liangjie Xia & Yongwan Bai & Sanjoy Ghose & Juanjuan Qin, 2022. "Differential game analysis of carbon emissions reduction and promotion in a sustainable supply chain considering social preferences," Annals of Operations Research, Springer, vol. 310(1), pages 257-292, March.
    13. John Mugambwa Serumaga-Zake & John Andrew van der Poll, 2021. "Addressing the Impact of Fourth Industrial Revolution on South African Manufacturing Small and Medium Enterprises (SMEs)," Sustainability, MDPI, vol. 13(21), pages 1-31, October.
    14. Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
    15. Fadi Shehab Shiyyab & Abdallah Bader Alzoubi & Qais Mohammad Obidat & Hashem Alshurafat, 2023. "The Impact of Artificial Intelligence Disclosure on Financial Performance," IJFS, MDPI, vol. 11(3), pages 1-25, September.
    16. Zhaoyuan He & Paul Turner, 2021. "A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities," Logistics, MDPI, vol. 5(4), pages 1-22, December.
    17. Andres Bustillo & Roberto Reis & Alisson R. Machado & Danil Yu. Pimenov, 2022. "Improving the accuracy of machine-learning models with data from machine test repetitions," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 203-221, January.
    18. Wurong Fu, 2021. "Macroscopic numerical model of reinforced concrete shear walls based on material properties," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1401-1410, June.
    19. Emilio Moretti & Elena Tappia & Veronique Limère & Marco Melacini, 2021. "Exploring the application of machine learning to the assembly line feeding problem," Operations Management Research, Springer, vol. 14(3), pages 403-419, December.
    20. Yue Wu & Dong-Shang Chang, 2024. "Decomposing the comprehensive efficiency of major cities into divisions on governance, ICT and sustainability: network slack-based measure model," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.

    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:33:y:2022:i:1:d:10.1007_s10845-020-01654-2. 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.