Choreographing the Dance of Decision Support: An Integrated Digital Twin and MCDM Framework for Predictive Maintenance in Smart Manufacturing

Qian Lian; Lu Zhang

doi:10.31181/dmame8120251463

Authors

Qian Lian Academy of Arts and Philosophy, Shinawatra University, Thailand https://orcid.org/0009-0006-7896-9024
Lu Zhang Academy of Arts and Philosophy, Shinawatra University, Thailand https://orcid.org/0009-0003-7720-5071

DOI:

https://doi.org/10.31181/dmame8120251463

Keywords:

Digital Twin (DT), Predictive Maintenance (PdM), Multi-Criteria Decision-Making (MCDM), Maintenance Planning, Analytic Hierarchy Process (AHP), Starling Murmuration Optimizer-Driven Multi-Kernel Support Vector Machine (SMO-MK-SVM)

Abstract

Digital Twin (DT) technologies have increasingly assumed a pivotal function within smart manufacturing, particularly in enhancing production efficiency, enabling real-time asset monitoring, and supporting predictive maintenance (PdM). Nevertheless, the conversion of substantial volumes of physical inspection data into actionable predictive models remains a significant challenge, especially concerning precision measurement and fault prevention. To confront this issue, the present study introduces an integrated Multi-Criteria Decision-Making and Digital Twin (MCDM-DT) framework aimed at facilitating predictive maintenance and offering effective decision support within smart manufacturing environments. The proposed framework is cloud-based, thereby enhancing system adaptability and responsiveness by synchronising real-time sensor outputs, inspection datasets, and virtual representations of assets. Machine learning algorithms, specifically a Starling Murmuration Optimiser-enhanced multi-kernel support vector machine (SMO-MK-SVM), are employed to assess equipment health and forecast potential failures with high accuracy. In parallel, MCDM methods, such as the Analytic Hierarchy Process (AHP), are utilised to assist in strategic maintenance planning. These methods evaluate various parameters including failure probabilities, potential downtime costs, inspection durations, and resource requirements, thereby enabling the ranking and prioritisation of maintenance tasks. By combining DT with MCDM, the proposed system offers a robust and comprehensive predictive maintenance solution, achieving enhanced predictive accuracy (98.2%) while ensuring efficient resource allocation and scheduling. This framework presents a scalable and practical tool for manufacturers seeking to adopt a proactive maintenance strategy, ultimately reducing equipment downtime, increasing operational efficiency, and improving overall product quality within smart manufacturing systems.

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