TY - GEN
T1 - An Ontology-Based Product Modelling Method for Smart Remanufacturing
AU - Hu, Youxi
AU - Liu, Chao
AU - Zhang, Ming
AU - Lu, Yuqian
AU - Jia, Yu
AU - Xu, Yuchun
PY - 2023/9/28
Y1 - 2023/9/28
N2 - Remanufacturing is widely recognized as a sustainable manufacturing paradigm that recovers residual value and extends the life cycle of end-of-life (EoL) products through a series of manufacturing processes. However, the uncertain and variable conditions of EoL products can affect decision-making in remanufacturing processes. Furthermore, related information about EOL products is difficult to store and transmit between original equipment manufacturers, end users, and remanufacturers. Currently, there is no standard method for unifying and managing this information. To address this issue, this research proposes an ontology-based product modelling method in a remanufacturing environment, with the goal of developing a standard unified product knowledge base. This knowledge base can be used to store and access information about EOL products, thus supporting decision-making in remanufacturing processes. Establishing an ontology-based product model for remanufacturing poses two challenges. Firstly, it is challenging to establish the EoL product ontology due to uncertainty and variable conditions of EoL products. Secondly, the decision-makings for different remanufacturing processes require distinct domain-specific knowledges, making it challenging to establish a comprehensive product ontology model to support the entire remanufacturing process. To address these challenges, a framework is established to illustrate the implementation and interaction of EoL product ontology with various remanufacturing processes. The EoL product ontology modeling process and rule-based reasoning methods are introduced. Finally, as a case study, a partial EoL product ontology is established for the disassembly process in remanufacturing to validate the proposed approach.
AB - Remanufacturing is widely recognized as a sustainable manufacturing paradigm that recovers residual value and extends the life cycle of end-of-life (EoL) products through a series of manufacturing processes. However, the uncertain and variable conditions of EoL products can affect decision-making in remanufacturing processes. Furthermore, related information about EOL products is difficult to store and transmit between original equipment manufacturers, end users, and remanufacturers. Currently, there is no standard method for unifying and managing this information. To address this issue, this research proposes an ontology-based product modelling method in a remanufacturing environment, with the goal of developing a standard unified product knowledge base. This knowledge base can be used to store and access information about EOL products, thus supporting decision-making in remanufacturing processes. Establishing an ontology-based product model for remanufacturing poses two challenges. Firstly, it is challenging to establish the EoL product ontology due to uncertainty and variable conditions of EoL products. Secondly, the decision-makings for different remanufacturing processes require distinct domain-specific knowledges, making it challenging to establish a comprehensive product ontology model to support the entire remanufacturing process. To address these challenges, a framework is established to illustrate the implementation and interaction of EoL product ontology with various remanufacturing processes. The EoL product ontology modeling process and rule-based reasoning methods are introduced. Finally, as a case study, a partial EoL product ontology is established for the disassembly process in remanufacturing to validate the proposed approach.
UR - https://ieeexplore.ieee.org/document/10260547
U2 - 10.1109/case56687.2023.10260547
DO - 10.1109/case56687.2023.10260547
M3 - Conference publication
T3 - CASE Proceedings
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)
PB - IEEE
T2 - 19th International Conference on Automation Science and Engineering
Y2 - 26 August 2023 through 30 August 2023
ER -