Disassembly Plan Representation by Hypergraph

To be successful in a circular economy, it is important to keep the cost of operationalizing remanufacturing processes low in order to retain as much value of the product as possible. Optimizing operations for disassembly, as a key process step, is therefore an important prerequisite for economically viable circular manufacturing. The generation of fit-to-resource disassembly instructions is labor-intensive and challenging because (digital) product information is often lacking at End-of-Life. With upcoming EU regulations for Eco-design for Sustainable Products in mind, including the future use of Digital Product Passports, it is time to think about standardized methods to capture disassembly information for products. First requirements from small and medium-sized remanufacturing companies have been collected and compared with available frameworks for modeling product topology, parameters, and (dis)assembly process rationale. Based on this, the disassembly hypergraph is presented as a concept for recording ‘resource-agnostic disassembly guides’ in (machine-readable) product models to determine required disassembly actions and tools ‘smartly’. The concept builds upon existing models. Additionally, suitable methods for the collection of disassembly information are explored, resulting in preliminary insights from disassembly data collection workshops. Although the approach is promising, future work is needed to expand the concept of the disassembly hypergraph with both guidelines for setting up disassembly ontologies and further systematic disassembly knowledge extraction in order to apply this as a useful means for companies to rationalize their disassembly operations.

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Publication Details

Journal
Automation
Published
2025-02-20
DOI
https://doi.org/10.3390/automation6010010
Citations
1
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
1.24

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article

Disassembly Plan Representation by Hypergraph

Jenny Coenen, Mirjam Zijderveld, Abboy Verkuilen, Niels de Buck
1 citations
Automation
Manufacturing Process and Optimization
1.24
article

Disassembly Plan Representation by Hypergraph

Jenny Coenen, Mirjam Zijderveld, Abboy Verkuilen, Niels de Buck
article en
1 citations

Abstract

To be successful in a circular economy, it is important to keep the cost of operationalizing remanufacturing processes low in order to retain as much value of the product as possible. Optimizing operations for disassembly, as a key process step, is therefore an important prerequisite for economically viable circular manufacturing. The generation of fit-to-resource disassembly instructions is labor-intensive and challenging because (digital) product information is often lacking at End-of-Life. With upcoming EU regulations for Eco-design for Sustainable Products in mind, including the future use of Digital Product Passports, it is time to think about standardized methods to capture disassembly information for products. First requirements from small and medium-sized remanufacturing companies have been collected and compared with available frameworks for modeling product topology, parameters, and (dis)assembly process rationale. Based on this, the disassembly hypergraph is presented as a concept for recording ‘resource-agnostic disassembly guides’ in (machine-readable) product models to determine required disassembly actions and tools ‘smartly’. The concept builds upon existing models. Additionally, suitable methods for the collection of disassembly information are explored, resulting in preliminary insights from disassembly data collection workshops. Although the approach is promising, future work is needed to expand the concept of the disassembly hypergraph with both guidelines for setting up disassembly ontologies and further systematic disassembly knowledge extraction in order to apply this as a useful means for companies to rationalize their disassembly operations.

AutomationVol. 6(1)
The Hague University of Applied Sciences (NL)
Hague University of Applied Sciences
Openalex Percentile: Top 22%
Manufacturing Process and Optimization
1.24
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