Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory
arXiv:2606.05334v1 Announce Type: new Abstract: Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cannot be decided from the current inspection alone, because future function fulfillment and component integrity may evolve differently under the next service scenario. Existing PHM approaches support degradation prediction, but often target fixed operating conditions or isolated component benchmarks, while material-fatigue assessment is rarely linked to system-level functional prognosis. This paper a
The increasing focus on circular economy principles necessitates more sophisticated methods for assessing the reuse potential of returned products, driving innovation in predictive maintenance.
Accurate prediction of functional behavior and material fatigue is crucial for optimizing reuse in circular factories, which can significantly impact sustainability, resource efficiency, and manufacturing costs.
This research provides a framework to move beyond isolated degradation predictions to system-level functional prognoses, enabling better decision-making for product reuse.
- · Circular Economy Manufacturers
- · Predictive Maintenance Software Developers
- · Materials Science Researchers
- · AI/ML Engineering Firms
- · Traditional Linear Production Models
- · Waste Management Industries (long-term decline)
- · Companies with Poor Material Traceability
Improved lifecycle assessment and operational efficiency for returned products in manufacturing.
Reduced demand for virgin materials and decreased industrial waste due to enhanced reuse capabilities.
Transformation of supply chains towards more integrated and data-driven circular models, potentially fostering new industrial ecosystems.
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Read at arXiv cs.AI