Timing Planning Knowledge Representation for Micro Assembly Based on Ontology
Micro assembly is a focal point in assembly research because of its high assembly accuracy requirements and high assembly difficulty. Current micro assembly techniques focus on precision and consistency, yet face a significant efficiency obstacle due to the conflict between precision and efficiency....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10878973/ |
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| Summary: | Micro assembly is a focal point in assembly research because of its high assembly accuracy requirements and high assembly difficulty. Current micro assembly techniques focus on precision and consistency, yet face a significant efficiency obstacle due to the conflict between precision and efficiency. Therefore, recent micro assembly challenges mainly include prolonged planning cycles, complex assembly processes, and suboptimal efficiency. An effective method to solve the efficiency problem is assembly timing intelligent decision-making, which can realize assembly efficiency optimization intelligently while ensuring assembly feasibility. As knowledge modeling is essential to intelligent decision-making, this paper proposes a micro assembly timing planning knowledge representation method. Firstly, micro assembly process knowledge is analyzed from two aspects: microdevices assembly process knowledge and micro assembly system process knowledge. Then, ontology-based micro assembly timing knowledge modeling method is proposed to realize the systematic knowledge representation for micro assembly timing planning, which is elaborated by the top-level micro assembly process ontology and micro assembly timing planning ontology. In conclusion, the proposed knowledge modeling theory and method present practical and implementable technical approaches for intelligent decision-making in timing planning. In addition, the knowledge modeling method from top-level ontology to application ontology provides the knowledge modeling logic and knowledge modeling process respectively. |
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| ISSN: | 2169-3536 |