홀로닉 팩토리를 위한 자산관리쉘 기반 가상화 모델Asset Administration Shell-based Virtualized Model for Holonic Factory
- Other Titles
- Asset Administration Shell-based Virtualized Model for Holonic Factory
- Authors
- 강영신; 박재현; 신승준; 김철호
- Issue Date
- Mar-2025
- Publisher
- 한국정밀공학회
- Keywords
- 홀로닉 제조 시스템; 자산관리쉘; 계약망 프로토콜; 공정 계획; 예측적 분석; Holonic manufacturing systems; Asset administration shell; Contract net protocol; Process planning; Predictive analytics
- Citation
- 한국정밀공학회지, v.42, no.3, pp 203 - 213
- Pages
- 11
- Indexed
- SCOPUS
KCI
- Journal Title
- 한국정밀공학회지
- Volume
- 42
- Number
- 3
- Start Page
- 203
- End Page
- 213
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/206894
- DOI
- 10.7736/JKSPE.024.102
- ISSN
- 1225-9071
2287-8769
- Abstract
- Holonic Manufacturing Systems (HMSs) are regarded as a foundation of cyber-physical production systems as they enablecomputers to conduct intelligent process planning, scheduling, and control by endowing manufacturing components withautonomy and collaboration. In an HMS, autonomy is realized by specifying holons that represent virtual agents ofmanufacturing components, while collaboration is facilitated through a communication mechanism that enables dataexchange and decision making throughout a holarchy of holons without human intervention. This study presents thedevelopment of a virtualized holon model and a predictive process planning procedure using the Asset Administration Shell(AAS), i.e., a standardized model that can identify digital representation of manufacturing components to ensureinteroperability. Specifically, an AAS-based information model was proposed to define operator, machine, product, and orderholons. In addition, a predictive process planning procedure based on the Contract Net Protocol was developed toautomatically allocate tasks while predicting task execution times. This study can contribute to the designing of an AASdomainspecific information model for HMS to increase interoperability in the holon holarchy, exhibiting the feasibility of AASapplications in predictive process planning on HMS.
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