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<p>Digital twins (DTs) are vital tools to (1) model products or systems at their design stages and (2) monitor systems’ conditions and predict their lifespans in their deployment stages. However, not many real-world case studies of DTs have been reported in literature to show significant benefits to <i>Small and Medium sized Enterprises</i> (SMEs). In this paper, DTs are proposed to predict the lifespans of used resources to pursue the sustainability of <i>Reconfigurable Manufacturing Systems</i> (RMSs). To illustrate the potential applications of DTs in enhancing the sustainability of real-world SMEs, a <i>Technical Assistive Project</i> (TAP) for the assessment of lifespans of materials storage racks is presented. The background and data collection methods are introduced, and digital models for 4 selective racks are developed to evaluate their <i>Factor of Safety</i> (FoS) subject to the worst loading scenarios. Parametric design studies are defined and conducted to investigate the dependence of FoS on varying loads. The results support managers’ scientific decision-makings on reusing and recycling manufacturing resources in Sustainable Manufacturing.</p>

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Digital twins for lifespan prediction of used storage racks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

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  • Zhuming Bi,
  • Donald Mueller,
  • Bin Chen,
  • Chaomin Luo,
  • Muzi Li

摘要

Digital twins (DTs) are vital tools to (1) model products or systems at their design stages and (2) monitor systems’ conditions and predict their lifespans in their deployment stages. However, not many real-world case studies of DTs have been reported in literature to show significant benefits to Small and Medium sized Enterprises (SMEs). In this paper, DTs are proposed to predict the lifespans of used resources to pursue the sustainability of Reconfigurable Manufacturing Systems (RMSs). To illustrate the potential applications of DTs in enhancing the sustainability of real-world SMEs, a Technical Assistive Project (TAP) for the assessment of lifespans of materials storage racks is presented. The background and data collection methods are introduced, and digital models for 4 selective racks are developed to evaluate their Factor of Safety (FoS) subject to the worst loading scenarios. Parametric design studies are defined and conducted to investigate the dependence of FoS on varying loads. The results support managers’ scientific decision-makings on reusing and recycling manufacturing resources in Sustainable Manufacturing.