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Product design and manufacturing process based ontology for manufacturing knowledge reuse

Author

Listed:
  • Peter Chhim

    (Wayne State University)

  • Ratna Babu Chinnam

    (Wayne State University)

  • Noureddin Sadawi

    (Imperial College London)

Abstract

This paper presents an effective product design and manufacturing process based ontology for manufacturing knowledge reuse. While a number of related efforts exist in the literature, there lacks a granular, interconnected product design and manufacturing process based ontology that can lead to greater industry adoption and knowledge reuse. In particular, the proposed ontology leverages an established industry standard to connect product design and manufacturing process knowledge in a logical and effective manner. The resulting effort is a general ontological framework that can be widely employed by the manufacturing industry. Additionally, the feasibility of implementing the ontology enabled knowledge reuse framework is demonstrated through a real-world case study.

Suggested Citation

  • Peter Chhim & Ratna Babu Chinnam & Noureddin Sadawi, 2019. "Product design and manufacturing process based ontology for manufacturing knowledge reuse," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 905-916, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1290-2
    DOI: 10.1007/s10845-016-1290-2
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    References listed on IDEAS

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    1. Ricardo Jardim-Goncalves & Antonio Grilo & Keith Popplewell, 2016. "Novel strategies for global manufacturing systems interoperability," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 1-9, February.
    2. Nitesh Khilwani & J. A. Harding, 2016. "Managing corporate memory on the semantic web," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 101-118, February.
    3. I.O. Sanya & E.M. Shehab, 2015. "A framework for developing engineering design ontologies within the aerospace industry," International Journal of Production Research, Taylor & Francis Journals, vol. 53(8), pages 2383-2409, April.
    4. Desouza, Kevin & Evaristo, Roberto, 2003. "Global Knowledge Management Strategies," European Management Journal, Elsevier, vol. 21(1), pages 62-67, February.
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    Cited by:

    1. Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
    2. Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.
    3. Yongjun Ji & Zuhua Jiang & Xinyu Li & Yongwen Huang & Fuhua Wang, 2023. "A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1615-1637, April.
    4. 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.
    5. Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.
    6. 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.

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