An ontology-based method of knowledge modelling for remanufacturing process planning

Yan He, Chuanpeng Hao, Yulin Wang, Yufeng Li, Yan Wang, Lingyu Huang, Xiaocheng Tian

Research output: Contribution to journalArticlepeer-review


Remanufacturing that returns used products to a like-new condition with equivalent warranty to match is an emerging triple-win (environmental, economic and social) industry. Process planning plays a vital role in the success of remanufacturing. However, compared with traditional mass manufacturing, the design of remanufacturing process planning (RPP) is far more complex and time-consuming, heavily depending on the experiences of operators. Since each returned used product, namely the raw materials for remanufacturing, is different, a customized RPP tackling the individuality of returned used products is essential. To this end, the reuse of remanufacturing knowledge from past successful RPP could lead to efficient generation of new process planning for new arrivals. This paper proposes an ontology-based method for knowledge modelling for RPP rapidly. In this method, (1) remanufacturing-ontology provides a unified framework for the management of information and knowledge from various sources. Especially, the remanufacturing knowledge modelling including problem description and problem solution is constructed via a remanufacturing semantic model; (2) Case-Based Reasoning (CBR) method is applied to reuse the knowledge from the most similar previous successful remanufacturing case for the rapid generation of RPP, leading to considerable time and cost saving. An application program is also presented to realize the proposed method. In addition, a case study of crankshaft remanufacturing is carried out to verify the feasibility and efficiency of the proposed method.
Original languageEnglish
Article number120952
Pages (from-to)1-11
JournalJournal of Cleaner Production
Publication statusPublished - 6 Mar 2020


  • Ontology
  • Knowledge modelling
  • Knowledge reuse
  • Remanufacturing process planning
  • CBR


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